Overview

Brought to you by YData

Dataset statistics

Number of variables67
Number of observations4517197
Missing cells146874370
Missing cells (%)48.5%
Total size in memory2.3 GiB
Average record size in memory536.0 B

Variable types

Text67

Dataset

DescriptionUS NMNH Extant Specimen Records 0052487-241126133413365
URLhttps://doi.org/10.15468/hnhrg3

Alerts

institutionID has constant value "urn:lsid:biocol.org:col:15463" Constant
collectionID has constant value "urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8" Constant
institutionCode has constant value "US" Constant
collectionCode has constant value "US" Constant
datasetName has constant value "NMNH Extant Biology" Constant
catalogNumber has 604870 (13.4%) missing values Missing
recordedBy has 54396 (1.2%) missing values Missing
lifeStage has 4154007 (92.0%) missing values Missing
preparations has 4382713 (97.0%) missing values Missing
associatedMedia has 318245 (7.0%) missing values Missing
associatedSequences has 4516846 (> 99.9%) missing values Missing
occurrenceRemarks has 4425636 (98.0%) missing values Missing
fieldNumber has 4516936 (> 99.9%) missing values Missing
eventDate has 499670 (11.1%) missing values Missing
startDayOfYear has 708176 (15.7%) missing values Missing
endDayOfYear has 706537 (15.6%) missing values Missing
year has 499670 (11.1%) missing values Missing
month has 700285 (15.5%) missing values Missing
day has 1180412 (26.1%) missing values Missing
verbatimEventDate has 2996092 (66.3%) missing values Missing
habitat has 4010721 (88.8%) missing values Missing
locationID has 4475514 (99.1%) missing values Missing
continent has 66179 (1.5%) missing values Missing
waterBody has 4497616 (99.6%) missing values Missing
islandGroup has 4404567 (97.5%) missing values Missing
island has 4140568 (91.7%) missing values Missing
stateProvince has 1002532 (22.2%) missing values Missing
county has 3779982 (83.7%) missing values Missing
locality has 332140 (7.4%) missing values Missing
minimumElevationInMeters has 2861975 (63.4%) missing values Missing
maximumElevationInMeters has 4018797 (89.0%) missing values Missing
minimumDepthInMeters has 4477155 (99.1%) missing values Missing
maximumDepthInMeters has 4480489 (99.2%) missing values Missing
verbatimDepth has 4495553 (99.5%) missing values Missing
decimalLatitude has 3846770 (85.2%) missing values Missing
decimalLongitude has 3846771 (85.2%) missing values Missing
geodeticDatum has 4487390 (99.3%) missing values Missing
coordinateUncertaintyInMeters has 4510725 (99.9%) missing values Missing
verbatimLatitude has 4479199 (99.2%) missing values Missing
verbatimLongitude has 4479213 (99.2%) missing values Missing
verbatimCoordinateSystem has 4480154 (99.2%) missing values Missing
georeferenceProtocol has 4390038 (97.2%) missing values Missing
georeferenceRemarks has 4516686 (> 99.9%) missing values Missing
identificationQualifier has 4506191 (99.8%) missing values Missing
typeStatus has 4400798 (97.4%) missing values Missing
identifiedBy has 3959451 (87.7%) missing values Missing
phylum has 3796598 (84.0%) missing values Missing
class has 166473 (3.7%) missing values Missing
order has 53018 (1.2%) missing values Missing
family has 49037 (1.1%) missing values Missing
subgenus has 4517108 (> 99.9%) missing values Missing
infraspecificEpithet has 4197487 (92.9%) missing values Missing
taxonRank has 4197770 (92.9%) missing values Missing
scientificNameAuthorship has 491413 (10.9%) missing values Missing
gbifID has unique values Unique
occurrenceID has unique values Unique

Reproduction

Analysis started2025-03-26 20:21:33.723596
Analysis finished2025-03-26 20:24:37.814963
Duration3 minutes and 4.09 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

gbifID
Text

Unique 

Distinct4517197
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:39.813643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters45171970
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4517197 ?
Unique (%)100.0%

Sample

1st row1320179379
2nd row1675994101
3rd row2592240144
4th row2571494932
5th row3357270605
ValueCountFrequency (%)
1320179379 1
 
< 0.1%
1320180447 1
 
< 0.1%
3897771070 1
 
< 0.1%
1320181031 1
 
< 0.1%
1321730416 1
 
< 0.1%
1321730340 1
 
< 0.1%
3467345455 1
 
< 0.1%
1456364699 1
 
< 0.1%
1321730091 1
 
< 0.1%
1320184062 1
 
< 0.1%
Other values (4517187) 4517187
> 99.9%
2025-03-26T16:24:41.777455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6589906
14.6%
2 6303681
14.0%
3 5903700
13.1%
5 4293013
9.5%
6 3898073
8.6%
4 3892848
8.6%
7 3756421
8.3%
8 3665637
8.1%
0 3572236
7.9%
9 3296455
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45171970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6589906
14.6%
2 6303681
14.0%
3 5903700
13.1%
5 4293013
9.5%
6 3898073
8.6%
4 3892848
8.6%
7 3756421
8.3%
8 3665637
8.1%
0 3572236
7.9%
9 3296455
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45171970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6589906
14.6%
2 6303681
14.0%
3 5903700
13.1%
5 4293013
9.5%
6 3898073
8.6%
4 3892848
8.6%
7 3756421
8.3%
8 3665637
8.1%
0 3572236
7.9%
9 3296455
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45171970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6589906
14.6%
2 6303681
14.0%
3 5903700
13.1%
5 4293013
9.5%
6 3898073
8.6%
4 3892848
8.6%
7 3756421
8.3%
8 3665637
8.1%
0 3572236
7.9%
9 3296455
7.3%
Distinct180691
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:41.925205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters85826743
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59506 ?
Unique (%)1.3%

Sample

1st row2016-08-30 13:42:00
2nd row2022-10-26 17:57:00
3rd row2020-05-10 23:06:00
4th row2020-04-09 11:53:00
5th row2021-09-10 21:16:00
ValueCountFrequency (%)
2017-08-04 233284
 
2.6%
2022-10-26 209215
 
2.3%
2022-06-03 121785
 
1.3%
2022-09-08 97163
 
1.1%
2017-12-19 94261
 
1.0%
2022-06-02 84487
 
0.9%
2024-10-17 76753
 
0.8%
2016-08-29 71282
 
0.8%
2016-08-30 70073
 
0.8%
2019-07-12 61225
 
0.7%
Other values (3909) 7914866
87.6%
2025-03-26T16:24:42.122212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23070715
26.9%
2 11943302
13.9%
1 11304975
13.2%
- 9034394
 
10.5%
: 9034394
 
10.5%
4517197
 
5.3%
3 2961517
 
3.5%
8 2608121
 
3.0%
9 2572901
 
3.0%
4 2389229
 
2.8%
Other values (3) 6389998
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85826743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23070715
26.9%
2 11943302
13.9%
1 11304975
13.2%
- 9034394
 
10.5%
: 9034394
 
10.5%
4517197
 
5.3%
3 2961517
 
3.5%
8 2608121
 
3.0%
9 2572901
 
3.0%
4 2389229
 
2.8%
Other values (3) 6389998
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85826743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23070715
26.9%
2 11943302
13.9%
1 11304975
13.2%
- 9034394
 
10.5%
: 9034394
 
10.5%
4517197
 
5.3%
3 2961517
 
3.5%
8 2608121
 
3.0%
9 2572901
 
3.0%
4 2389229
 
2.8%
Other values (3) 6389998
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85826743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23070715
26.9%
2 11943302
13.9%
1 11304975
13.2%
- 9034394
 
10.5%
: 9034394
 
10.5%
4517197
 
5.3%
3 2961517
 
3.5%
8 2608121
 
3.0%
9 2572901
 
3.0%
4 2389229
 
2.8%
Other values (3) 6389998
 
7.4%

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:42.169960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters130998713
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:15463
2nd rowurn:lsid:biocol.org:col:15463
3rd rowurn:lsid:biocol.org:col:15463
4th rowurn:lsid:biocol.org:col:15463
5th rowurn:lsid:biocol.org:col:15463
ValueCountFrequency (%)
urn:lsid:biocol.org:col:15463 4517197
100.0%
2025-03-26T16:24:42.244296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 18068788
13.8%
: 18068788
13.8%
l 13551591
 
10.3%
i 9034394
 
6.9%
r 9034394
 
6.9%
c 9034394
 
6.9%
g 4517197
 
3.4%
6 4517197
 
3.4%
4 4517197
 
3.4%
5 4517197
 
3.4%
Other values (8) 36137576
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130998713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 18068788
13.8%
: 18068788
13.8%
l 13551591
 
10.3%
i 9034394
 
6.9%
r 9034394
 
6.9%
c 9034394
 
6.9%
g 4517197
 
3.4%
6 4517197
 
3.4%
4 4517197
 
3.4%
5 4517197
 
3.4%
Other values (8) 36137576
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130998713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 18068788
13.8%
: 18068788
13.8%
l 13551591
 
10.3%
i 9034394
 
6.9%
r 9034394
 
6.9%
c 9034394
 
6.9%
g 4517197
 
3.4%
6 4517197
 
3.4%
4 4517197
 
3.4%
5 4517197
 
3.4%
Other values (8) 36137576
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130998713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 18068788
13.8%
: 18068788
13.8%
l 13551591
 
10.3%
i 9034394
 
6.9%
r 9034394
 
6.9%
c 9034394
 
6.9%
g 4517197
 
3.4%
6 4517197
 
3.4%
4 4517197
 
3.4%
5 4517197
 
3.4%
Other values (8) 36137576
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:42.272294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters203273865
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
2nd rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
3rd rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
4th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
5th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
ValueCountFrequency (%)
urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8 4517197
100.0%
2025-03-26T16:24:42.348737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 18068788
 
8.9%
3 18068788
 
8.9%
- 18068788
 
8.9%
e 18068788
 
8.9%
6 13551591
 
6.7%
a 13551591
 
6.7%
u 13551591
 
6.7%
d 9034394
 
4.4%
2 9034394
 
4.4%
1 9034394
 
4.4%
Other values (10) 63240758
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203273865
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 18068788
 
8.9%
3 18068788
 
8.9%
- 18068788
 
8.9%
e 18068788
 
8.9%
6 13551591
 
6.7%
a 13551591
 
6.7%
u 13551591
 
6.7%
d 9034394
 
4.4%
2 9034394
 
4.4%
1 9034394
 
4.4%
Other values (10) 63240758
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203273865
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 18068788
 
8.9%
3 18068788
 
8.9%
- 18068788
 
8.9%
e 18068788
 
8.9%
6 13551591
 
6.7%
a 13551591
 
6.7%
u 13551591
 
6.7%
d 9034394
 
4.4%
2 9034394
 
4.4%
1 9034394
 
4.4%
Other values (10) 63240758
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203273865
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 18068788
 
8.9%
3 18068788
 
8.9%
- 18068788
 
8.9%
e 18068788
 
8.9%
6 13551591
 
6.7%
a 13551591
 
6.7%
u 13551591
 
6.7%
d 9034394
 
4.4%
2 9034394
 
4.4%
1 9034394
 
4.4%
Other values (10) 63240758
31.1%

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:42.375739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9034394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 4517197
100.0%
2025-03-26T16:24:42.450562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9034394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9034394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9034394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:42.477515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9034394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 4517197
100.0%
2025-03-26T16:24:42.550901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9034394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9034394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9034394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4517197
50.0%
S 4517197
50.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:42.578902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters85826743
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 4517197
33.3%
extant 4517197
33.3%
biology 4517197
33.3%
2025-03-26T16:24:42.654416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 9034394
 
10.5%
9034394
 
10.5%
t 9034394
 
10.5%
o 9034394
 
10.5%
M 4517197
 
5.3%
H 4517197
 
5.3%
E 4517197
 
5.3%
x 4517197
 
5.3%
a 4517197
 
5.3%
n 4517197
 
5.3%
Other values (5) 22585985
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85826743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 9034394
 
10.5%
9034394
 
10.5%
t 9034394
 
10.5%
o 9034394
 
10.5%
M 4517197
 
5.3%
H 4517197
 
5.3%
E 4517197
 
5.3%
x 4517197
 
5.3%
a 4517197
 
5.3%
n 4517197
 
5.3%
Other values (5) 22585985
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85826743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 9034394
 
10.5%
9034394
 
10.5%
t 9034394
 
10.5%
o 9034394
 
10.5%
M 4517197
 
5.3%
H 4517197
 
5.3%
E 4517197
 
5.3%
x 4517197
 
5.3%
a 4517197
 
5.3%
n 4517197
 
5.3%
Other values (5) 22585985
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85826743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 9034394
 
10.5%
9034394
 
10.5%
t 9034394
 
10.5%
o 9034394
 
10.5%
M 4517197
 
5.3%
H 4517197
 
5.3%
E 4517197
 
5.3%
x 4517197
 
5.3%
a 4517197
 
5.3%
n 4517197
 
5.3%
Other values (5) 22585985
26.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:42.681412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17.01103029
Min length16

Characters and Unicode

Total characters76842175
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPreservedSpecimen
2nd rowPreservedSpecimen
3rd rowPreservedSpecimen
4th rowPreservedSpecimen
5th rowPreservedSpecimen
ValueCountFrequency (%)
preservedspecimen 4467365
98.9%
machineobservation 49829
 
1.1%
humanobservation 3
 
< 0.1%
2025-03-26T16:24:42.773379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22436486
29.2%
r 8984562
11.7%
n 4567029
 
5.9%
i 4567026
 
5.9%
s 4517197
 
5.9%
v 4517197
 
5.9%
c 4517194
 
5.9%
m 4467368
 
5.8%
P 4467365
 
5.8%
p 4467365
 
5.8%
Other values (11) 9333386
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76842175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22436486
29.2%
r 8984562
11.7%
n 4567029
 
5.9%
i 4567026
 
5.9%
s 4517197
 
5.9%
v 4517197
 
5.9%
c 4517194
 
5.9%
m 4467368
 
5.8%
P 4467365
 
5.8%
p 4467365
 
5.8%
Other values (11) 9333386
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76842175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22436486
29.2%
r 8984562
11.7%
n 4567029
 
5.9%
i 4567026
 
5.9%
s 4517197
 
5.9%
v 4517197
 
5.9%
c 4517194
 
5.9%
m 4467368
 
5.8%
P 4467365
 
5.8%
p 4467365
 
5.8%
Other values (11) 9333386
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76842175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22436486
29.2%
r 8984562
11.7%
n 4567029
 
5.9%
i 4567026
 
5.9%
s 4517197
 
5.9%
v 4517197
 
5.9%
c 4517194
 
5.9%
m 4467368
 
5.8%
P 4467365
 
5.8%
p 4467365
 
5.8%
Other values (11) 9333386
12.1%

occurrenceID
Text

Unique 

Distinct4517197
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.5 MiB
2025-03-26T16:24:44.772939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters284583411
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4517197 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/383aab1ce-8b35-4007-8eba-472b592b7a99
2nd rowhttp://n2t.net/ark:/65665/3c8351e79-8b3b-4df0-80be-cb019ba60185
3rd rowhttp://n2t.net/ark:/65665/3c8377593-a51b-4b6a-835d-649053b2ef0f
4th rowhttp://n2t.net/ark:/65665/383b388e9-b7cc-4b41-95cc-e0a1b092179a
5th rowhttp://n2t.net/ark:/65665/3c83e5abc-b64e-45a4-aa42-faf5abc93792
ValueCountFrequency (%)
http://n2t.net/ark:/65665/383aab1ce-8b35-4007-8eba-472b592b7a99 1
 
< 0.1%
http://n2t.net/ark:/65665/383b6d73e-eb70-4b52-81b8-336878ca92f0 1
 
< 0.1%
http://n2t.net/ark:/65665/3c84a8b17-83ab-45b2-bb8e-ccea78a7e003 1
 
< 0.1%
http://n2t.net/ark:/65665/383be1f82-08fe-4004-9374-3793b1df97c5 1
 
< 0.1%
http://n2t.net/ark:/65665/3c842b3ee-b36e-41da-867e-a7c09def7524 1
 
< 0.1%
http://n2t.net/ark:/65665/3c841ed6b-df48-4633-aae7-d3e846a86aa3 1
 
< 0.1%
http://n2t.net/ark:/65665/383b77fe7-0ea2-407e-bde8-bba5ef603c4a 1
 
< 0.1%
http://n2t.net/ark:/65665/3c8411b46-27ee-4d70-ab07-e1bd72f2e83a 1
 
< 0.1%
http://n2t.net/ark:/65665/3c83f60ef-2f0d-451e-986a-e0c2dfb03675 1
 
< 0.1%
http://n2t.net/ark:/65665/383e13640-df35-46e1-befc-1068e49e2444 1
 
< 0.1%
Other values (4517187) 4517187
> 99.9%
2025-03-26T16:24:46.707484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 22585985
 
7.9%
6 22023807
 
7.7%
- 18068788
 
6.3%
t 18068788
 
6.3%
5 17503109
 
6.2%
a 14113945
 
5.0%
e 12992767
 
4.6%
4 12988088
 
4.6%
2 12986092
 
4.6%
3 12980339
 
4.6%
Other values (16) 120271703
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284583411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 22585985
 
7.9%
6 22023807
 
7.7%
- 18068788
 
6.3%
t 18068788
 
6.3%
5 17503109
 
6.2%
a 14113945
 
5.0%
e 12992767
 
4.6%
4 12988088
 
4.6%
2 12986092
 
4.6%
3 12980339
 
4.6%
Other values (16) 120271703
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284583411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 22585985
 
7.9%
6 22023807
 
7.7%
- 18068788
 
6.3%
t 18068788
 
6.3%
5 17503109
 
6.2%
a 14113945
 
5.0%
e 12992767
 
4.6%
4 12988088
 
4.6%
2 12986092
 
4.6%
3 12980339
 
4.6%
Other values (16) 120271703
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284583411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 22585985
 
7.9%
6 22023807
 
7.7%
- 18068788
 
6.3%
t 18068788
 
6.3%
5 17503109
 
6.2%
a 14113945
 
5.0%
e 12992767
 
4.6%
4 12988088
 
4.6%
2 12986092
 
4.6%
3 12980339
 
4.6%
Other values (16) 120271703
42.3%

catalogNumber
Text

Missing 

Distinct3683650
Distinct (%)94.2%
Missing604870
Missing (%)13.4%
Memory size34.5 MiB
2025-03-26T16:24:48.465817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length10
Mean length9.635912847
Min length4

Characters and Unicode

Total characters37698842
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3482924 ?
Unique (%)89.0%

Sample

1st rowUS 213621
2nd rowUS 2144946
3rd rowUS 3113222
4th rowUS 2583825
5th rowUS 3026466
ValueCountFrequency (%)
us 3869538
49.7%
sem 238
 
< 0.1%
146
 
< 0.1%
stub 135
 
< 0.1%
1 133
 
< 0.1%
micrograph 103
 
< 0.1%
169920 59
 
< 0.1%
2 44
 
< 0.1%
3 40
 
< 0.1%
95340 36
 
< 0.1%
Other values (3683257) 3912214
50.3%
2025-03-26T16:24:50.271423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3912702
10.4%
U 3912329
10.4%
3870359
10.3%
2 3435307
9.1%
1 3366889
8.9%
3 3066195
8.1%
5 2347540
 
6.2%
6 2338596
 
6.2%
4 2337699
 
6.2%
7 2292189
 
6.1%
Other values (38) 6819037
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37698842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3912702
10.4%
U 3912329
10.4%
3870359
10.3%
2 3435307
9.1%
1 3366889
8.9%
3 3066195
8.1%
5 2347540
 
6.2%
6 2338596
 
6.2%
4 2337699
 
6.2%
7 2292189
 
6.1%
Other values (38) 6819037
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37698842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3912702
10.4%
U 3912329
10.4%
3870359
10.3%
2 3435307
9.1%
1 3366889
8.9%
3 3066195
8.1%
5 2347540
 
6.2%
6 2338596
 
6.2%
4 2337699
 
6.2%
7 2292189
 
6.1%
Other values (38) 6819037
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37698842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3912702
10.4%
U 3912329
10.4%
3870359
10.3%
2 3435307
9.1%
1 3366889
8.9%
3 3066195
8.1%
5 2347540
 
6.2%
6 2338596
 
6.2%
4 2337699
 
6.2%
7 2292189
 
6.1%
Other values (38) 6819037
18.1%
Distinct483661
Distinct (%)10.8%
Missing39559
Missing (%)0.9%
Memory size34.5 MiB
2025-03-26T16:24:50.498368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length100
Median length93
Mean length4.492141392
Min length1

Characters and Unicode

Total characters20114183
Distinct characters126
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349378 ?
Unique (%)7.8%

Sample

1st rowBLM-210-IV-11-B-TDS
2nd row4319
3rd row2429
4th row95426
5th row1414/512
ValueCountFrequency (%)
s.n 643853
 
13.5%
bureau 20605
 
0.4%
eyd 15910
 
0.3%
s 14315
 
0.3%
of 13870
 
0.3%
n 13796
 
0.3%
science 13414
 
0.3%
d&ml 12901
 
0.3%
12509
 
0.3%
h 8675
 
0.2%
Other values (337655) 3988668
83.8%
2025-03-26T16:24:50.802026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2408806
12.0%
2 1865733
9.3%
3 1606812
 
8.0%
4 1507781
 
7.5%
0 1450667
 
7.2%
5 1449885
 
7.2%
6 1399895
 
7.0%
. 1359971
 
6.8%
7 1317624
 
6.6%
8 1265202
 
6.3%
Other values (116) 4481807
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20114183
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2408806
12.0%
2 1865733
9.3%
3 1606812
 
8.0%
4 1507781
 
7.5%
0 1450667
 
7.2%
5 1449885
 
7.2%
6 1399895
 
7.0%
. 1359971
 
6.8%
7 1317624
 
6.6%
8 1265202
 
6.3%
Other values (116) 4481807
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20114183
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2408806
12.0%
2 1865733
9.3%
3 1606812
 
8.0%
4 1507781
 
7.5%
0 1450667
 
7.2%
5 1449885
 
7.2%
6 1399895
 
7.0%
. 1359971
 
6.8%
7 1317624
 
6.6%
8 1265202
 
6.3%
Other values (116) 4481807
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20114183
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2408806
12.0%
2 1865733
9.3%
3 1606812
 
8.0%
4 1507781
 
7.5%
0 1450667
 
7.2%
5 1449885
 
7.2%
6 1399895
 
7.0%
. 1359971
 
6.8%
7 1317624
 
6.6%
8 1265202
 
6.3%
Other values (116) 4481807
22.3%

recordedBy
Text

Missing 

Distinct148184
Distinct (%)3.3%
Missing54396
Missing (%)1.2%
Memory size34.5 MiB
2025-03-26T16:24:50.949574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length207
Median length182
Mean length17.2484191
Min length1

Characters and Unicode

Total characters76976262
Distinct characters169
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68047 ?
Unique (%)1.5%

Sample

1st rowContinental Shelf Associates for the MMS/BLM
2nd rowJ. Soukup
3rd rowI. Morel
4th rowJ. Steyermark & Cora Steyermark
5th rowA. Oakes & -. Ellis
ValueCountFrequency (%)
1251158
 
7.3%
j 893353
 
5.2%
a 765793
 
4.5%
r 679244
 
4.0%
e 679077
 
4.0%
c 634176
 
3.7%
m 612564
 
3.6%
h 550652
 
3.2%
l 447746
 
2.6%
w 441837
 
2.6%
Other values (47914) 10067791
59.1%
2025-03-26T16:24:51.155670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12560590
16.3%
. 9152960
 
11.9%
e 4948553
 
6.4%
r 3621737
 
4.7%
a 3598240
 
4.7%
o 3036452
 
3.9%
n 3021384
 
3.9%
l 2883072
 
3.7%
i 2490019
 
3.2%
t 2010463
 
2.6%
Other values (159) 29652792
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76976262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12560590
16.3%
. 9152960
 
11.9%
e 4948553
 
6.4%
r 3621737
 
4.7%
a 3598240
 
4.7%
o 3036452
 
3.9%
n 3021384
 
3.9%
l 2883072
 
3.7%
i 2490019
 
3.2%
t 2010463
 
2.6%
Other values (159) 29652792
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76976262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12560590
16.3%
. 9152960
 
11.9%
e 4948553
 
6.4%
r 3621737
 
4.7%
a 3598240
 
4.7%
o 3036452
 
3.9%
n 3021384
 
3.9%
l 2883072
 
3.7%
i 2490019
 
3.2%
t 2010463
 
2.6%
Other values (159) 29652792
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76976262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12560590
16.3%
. 9152960
 
11.9%
e 4948553
 
6.4%
r 3621737
 
4.7%
a 3598240
 
4.7%
o 3036452
 
3.9%
n 3021384
 
3.9%
l 2883072
 
3.7%
i 2490019
 
3.2%
t 2010463
 
2.6%
Other values (159) 29652792
38.5%
Distinct22
Distinct (%)< 0.1%
Missing560
Missing (%)< 0.1%
Memory size34.5 MiB
2025-03-26T16:24:51.190531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.000007971
Min length1

Characters and Unicode

Total characters4516673
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1 4515430
> 99.9%
2 489
 
< 0.1%
0 306
 
< 0.1%
3 137
 
< 0.1%
4 94
 
< 0.1%
5 55
 
< 0.1%
6 40
 
< 0.1%
7 21
 
< 0.1%
8 16
 
< 0.1%
9 13
 
< 0.1%
Other values (12) 36
 
< 0.1%
2025-03-26T16:24:51.267400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4515471
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4516673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4515471
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4516673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4515471
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4516673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4515471
> 99.9%
2 496
 
< 0.1%
0 315
 
< 0.1%
3 142
 
< 0.1%
4 96
 
< 0.1%
5 57
 
< 0.1%
6 42
 
< 0.1%
7 22
 
< 0.1%
8 17
 
< 0.1%
9 15
 
< 0.1%

lifeStage
Text

Missing 

Distinct117
Distinct (%)< 0.1%
Missing4154007
Missing (%)92.0%
Memory size34.5 MiB
2025-03-26T16:24:51.296114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length9
Mean length10.2279468
Min length1

Characters and Unicode

Total characters3714688
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowFruiting
2nd rowIn bud
3rd rowFlowering
4th rowFlowering
5th rowImmature fruit
ValueCountFrequency (%)
flowering 233661
50.4%
fruiting 101132
21.8%
and 42701
 
9.2%
vegetative 23872
 
5.1%
fertile 18494
 
4.0%
in 8664
 
1.9%
bud 8101
 
1.7%
flower 7511
 
1.6%
fruit 7392
 
1.6%
sterile 3318
 
0.7%
Other values (52) 9209
 
2.0%
2025-03-26T16:24:51.388556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 489273
13.2%
n 386481
10.4%
r 377756
10.2%
e 366119
9.9%
g 358944
9.7%
F 358799
9.7%
l 267519
7.2%
o 243856
6.6%
w 243641
6.6%
t 181888
 
4.9%
Other values (34) 440412
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3714688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 489273
13.2%
n 386481
10.4%
r 377756
10.2%
e 366119
9.9%
g 358944
9.7%
F 358799
9.7%
l 267519
7.2%
o 243856
6.6%
w 243641
6.6%
t 181888
 
4.9%
Other values (34) 440412
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3714688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 489273
13.2%
n 386481
10.4%
r 377756
10.2%
e 366119
9.9%
g 358944
9.7%
F 358799
9.7%
l 267519
7.2%
o 243856
6.6%
w 243641
6.6%
t 181888
 
4.9%
Other values (34) 440412
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3714688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 489273
13.2%
n 386481
10.4%
r 377756
10.2%
e 366119
9.9%
g 358944
9.7%
F 358799
9.7%
l 267519
7.2%
o 243856
6.6%
w 243641
6.6%
t 181888
 
4.9%
Other values (34) 440412
11.9%

preparations
Text

Missing 

Distinct117
Distinct (%)0.1%
Missing4382713
Missing (%)97.0%
Memory size34.5 MiB
2025-03-26T16:24:51.423793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length154
Median length142
Mean length13.21021832
Min length3

Characters and Unicode

Total characters1776563
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)< 0.1%

Sample

1st rowWood Sample
2nd rowPhotograph
3rd rowMicroslide
4th rowPhotograph
5th rowPhotograph; Photograph
ValueCountFrequency (%)
sample 42497
18.8%
wood 42494
18.8%
microslide 41842
18.5%
photograph 33535
14.8%
individual 18798
8.3%
strewn 10234
 
4.5%
sem 6926
 
3.1%
micrograph 6518
 
2.9%
ink 5467
 
2.4%
and 3022
 
1.3%
Other values (80) 15061
 
6.7%
2025-03-26T16:24:51.533687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 203941
 
11.5%
i 158065
 
8.9%
d 125599
 
7.1%
l 110721
 
6.2%
a 110109
 
6.2%
r 106228
 
6.0%
e 101644
 
5.7%
91910
 
5.2%
p 85893
 
4.8%
h 73889
 
4.2%
Other values (45) 608564
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1776563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 203941
 
11.5%
i 158065
 
8.9%
d 125599
 
7.1%
l 110721
 
6.2%
a 110109
 
6.2%
r 106228
 
6.0%
e 101644
 
5.7%
91910
 
5.2%
p 85893
 
4.8%
h 73889
 
4.2%
Other values (45) 608564
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1776563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 203941
 
11.5%
i 158065
 
8.9%
d 125599
 
7.1%
l 110721
 
6.2%
a 110109
 
6.2%
r 106228
 
6.0%
e 101644
 
5.7%
91910
 
5.2%
p 85893
 
4.8%
h 73889
 
4.2%
Other values (45) 608564
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1776563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 203941
 
11.5%
i 158065
 
8.9%
d 125599
 
7.1%
l 110721
 
6.2%
a 110109
 
6.2%
r 106228
 
6.0%
e 101644
 
5.7%
91910
 
5.2%
p 85893
 
4.8%
h 73889
 
4.2%
Other values (45) 608564
34.3%

associatedMedia
Text

Missing 

Distinct4174186
Distinct (%)99.4%
Missing318245
Missing (%)7.0%
Memory size34.5 MiB
2025-03-26T16:24:53.493094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1040
Median length49
Mean length49.74543743
Min length48

Characters and Unicode

Total characters208878704
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4153366 ?
Unique (%)98.9%

Sample

1st rowhttps://collections.nmnh.si.edu/media/?i=12410529
2nd rowhttps://collections.nmnh.si.edu/media/?i=14440219
3rd rowhttps://collections.nmnh.si.edu/media/?i=14306337
4th rowhttps://collections.nmnh.si.edu/media/?i=15522674
5th rowhttps://collections.nmnh.si.edu/media/?i=15293772
ValueCountFrequency (%)
16574494 50
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=13965384 42
 
< 0.1%
16580564 35
 
< 0.1%
16582219 30
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=15413125 25
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16645032 22
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=15413478 21
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=10422983 19
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=15921416 17
 
< 0.1%
https://collections.nmnh.si.edu/media/?i=16645082 16
 
< 0.1%
Other values (4475620) 4512347
> 99.9%
2025-03-26T16:24:55.553629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 16795808
 
8.0%
i 16795808
 
8.0%
s 12596856
 
6.0%
e 12596856
 
6.0%
n 12596856
 
6.0%
. 12596856
 
6.0%
t 12596856
 
6.0%
h 8397904
 
4.0%
c 8397904
 
4.0%
o 8397904
 
4.0%
Other values (21) 87109096
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208878704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 16795808
 
8.0%
i 16795808
 
8.0%
s 12596856
 
6.0%
e 12596856
 
6.0%
n 12596856
 
6.0%
. 12596856
 
6.0%
t 12596856
 
6.0%
h 8397904
 
4.0%
c 8397904
 
4.0%
o 8397904
 
4.0%
Other values (21) 87109096
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208878704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 16795808
 
8.0%
i 16795808
 
8.0%
s 12596856
 
6.0%
e 12596856
 
6.0%
n 12596856
 
6.0%
. 12596856
 
6.0%
t 12596856
 
6.0%
h 8397904
 
4.0%
c 8397904
 
4.0%
o 8397904
 
4.0%
Other values (21) 87109096
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208878704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 16795808
 
8.0%
i 16795808
 
8.0%
s 12596856
 
6.0%
e 12596856
 
6.0%
n 12596856
 
6.0%
. 12596856
 
6.0%
t 12596856
 
6.0%
h 8397904
 
4.0%
c 8397904
 
4.0%
o 8397904
 
4.0%
Other values (21) 87109096
41.7%

associatedSequences
Text

Missing 

Distinct334
Distinct (%)95.2%
Missing4516846
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-26T16:24:55.596631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length499
Median length249
Mean length140.8803419
Min length49

Characters and Unicode

Total characters49449
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)90.3%

Sample

1st rowhttps://www.ncbi.nlm.nih.gov/gquery?term=ON553270
2nd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553291
3rd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553246
4th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553283
5th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=EU527211|https://www.ncbi.nlm.nih.gov/gquery?term=EU527308|https://www.ncbi.nlm.nih.gov/gquery?term=EU527261
ValueCountFrequency (%)
https://www.ncbi.nlm.nih.gov/gquery?term=jn837179|https://www.ncbi.nlm.nih.gov/gquery?term=jn837463|https://www.ncbi.nlm.nih.gov/gquery?term=jn837359|https://www.ncbi.nlm.nih.gov/gquery?term=jn837269 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837192|https://www.ncbi.nlm.nih.gov/gquery?term=jn837282|https://www.ncbi.nlm.nih.gov/gquery?term=jn837372|https://www.ncbi.nlm.nih.gov/gquery?term=jn837475 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=eu527212|https://www.ncbi.nlm.nih.gov/gquery?term=eu527309|https://www.ncbi.nlm.nih.gov/gquery?term=eu527262 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837150|https://www.ncbi.nlm.nih.gov/gquery?term=jn837436|https://www.ncbi.nlm.nih.gov/gquery?term=jn837330|https://www.ncbi.nlm.nih.gov/gquery?term=jn837240 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837187|https://www.ncbi.nlm.nih.gov/gquery?term=jn837470|https://www.ncbi.nlm.nih.gov/gquery?term=jn837367|https://www.ncbi.nlm.nih.gov/gquery?term=jn837277 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=kf989555|https://www.ncbi.nlm.nih.gov/gquery?term=kf989872|https://www.ncbi.nlm.nih.gov/gquery?term=kf989774|https://www.ncbi.nlm.nih.gov/gquery?term=kf989974|https://www.ncbi.nlm.nih.gov/gquery?term=kf989663 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837109|https://www.ncbi.nlm.nih.gov/gquery?term=jn837391|https://www.ncbi.nlm.nih.gov/gquery?term=jn837290|https://www.ncbi.nlm.nih.gov/gquery?term=jn837199 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=eu527241|https://www.ncbi.nlm.nih.gov/gquery?term=eu527291 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837183|https://www.ncbi.nlm.nih.gov/gquery?term=jn837467|https://www.ncbi.nlm.nih.gov/gquery?term=jn837363|https://www.ncbi.nlm.nih.gov/gquery?term=jn837273 2
 
0.6%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837191|https://www.ncbi.nlm.nih.gov/gquery?term=jn837281|https://www.ncbi.nlm.nih.gov/gquery?term=jn837371|https://www.ncbi.nlm.nih.gov/gquery?term=jn837474 2
 
0.6%
Other values (324) 331
94.3%
2025-03-26T16:24:55.685006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3984
 
8.1%
t 2988
 
6.0%
/ 2988
 
6.0%
w 2988
 
6.0%
n 2988
 
6.0%
h 1992
 
4.0%
i 1992
 
4.0%
r 1992
 
4.0%
m 1992
 
4.0%
g 1992
 
4.0%
Other values (40) 23553
47.6%

occurrenceRemarks
Text

Missing 

Distinct28697
Distinct (%)31.3%
Missing4425636
Missing (%)98.0%
Memory size34.5 MiB
2025-03-26T16:24:55.828684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4263
Median length2284
Mean length76.44932886
Min length1

Characters and Unicode

Total characters6999777
Distinct characters140
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24548 ?
Unique (%)26.8%

Sample

1st rowReceived as: seed
2nd rowTranscribed by digital volunteers
3rd rowBRG
4th rowTranscribed by digital volunteers; Original spelling as annotated and published is "subplebeia". Same (?) taxon re-published in Contr. U.S. Natl. Herb. 17: 46 (1913) with more explicit type citation. Unclear whether Lecidea subplebeia is a later homonym of Lecidea subplebeja Vain. (1890); Lecidea austrocalifornica Zahlbr. published as replacement name but citing Lecidea "subplebeja Nyl. apud Hasse". The latter name is superfluous if the original name is not a later homonym.
5th rowUS, NY
ValueCountFrequency (%)
by 38275
 
3.8%
transcribed 30358
 
3.0%
digital 30048
 
3.0%
volunteers 30031
 
3.0%
19533
 
1.9%
of 17290
 
1.7%
us 14363
 
1.4%
as 13942
 
1.4%
and 12949
 
1.3%
the 12016
 
1.2%
Other values (44358) 793437
78.4%
2025-03-26T16:24:56.041900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
920681
 
13.2%
e 563234
 
8.0%
a 439319
 
6.3%
i 406579
 
5.8%
t 346218
 
4.9%
n 335884
 
4.8%
o 334321
 
4.8%
r 328528
 
4.7%
l 292743
 
4.2%
s 268201
 
3.8%
Other values (130) 2764069
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6999777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
920681
 
13.2%
e 563234
 
8.0%
a 439319
 
6.3%
i 406579
 
5.8%
t 346218
 
4.9%
n 335884
 
4.8%
o 334321
 
4.8%
r 328528
 
4.7%
l 292743
 
4.2%
s 268201
 
3.8%
Other values (130) 2764069
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6999777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
920681
 
13.2%
e 563234
 
8.0%
a 439319
 
6.3%
i 406579
 
5.8%
t 346218
 
4.9%
n 335884
 
4.8%
o 334321
 
4.8%
r 328528
 
4.7%
l 292743
 
4.2%
s 268201
 
3.8%
Other values (130) 2764069
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6999777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
920681
 
13.2%
e 563234
 
8.0%
a 439319
 
6.3%
i 406579
 
5.8%
t 346218
 
4.9%
n 335884
 
4.8%
o 334321
 
4.8%
r 328528
 
4.7%
l 292743
 
4.2%
s 268201
 
3.8%
Other values (130) 2764069
39.5%

fieldNumber
Text

Missing 

Distinct14
Distinct (%)5.4%
Missing4516936
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-26T16:24:56.079899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length9
Mean length9.134099617
Min length4

Characters and Unicode

Total characters2384
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)2.7%

Sample

1st rowSample OY
2nd rowSample OY
3rd rowSample OY
4th rowSample OY
5th rowSample OY
ValueCountFrequency (%)
sample 240
46.2%
oy 240
46.2%
koolau 5
 
1.0%
b 4
 
0.8%
a 4
 
0.8%
259 3
 
0.6%
l-52 3
 
0.6%
koolau_784 3
 
0.6%
17-v-88-5-n 2
 
0.4%
zeeland 2
 
0.4%
Other values (12) 14
 
2.7%
2025-03-26T16:24:56.166329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 263
11.0%
259
10.9%
l 255
10.7%
e 247
10.4%
S 241
10.1%
p 240
10.1%
m 240
10.1%
O 240
10.1%
Y 240
10.1%
o 19
 
0.8%
Other values (33) 140
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 263
11.0%
259
10.9%
l 255
10.7%
e 247
10.4%
S 241
10.1%
p 240
10.1%
m 240
10.1%
O 240
10.1%
Y 240
10.1%
o 19
 
0.8%
Other values (33) 140
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 263
11.0%
259
10.9%
l 255
10.7%
e 247
10.4%
S 241
10.1%
p 240
10.1%
m 240
10.1%
O 240
10.1%
Y 240
10.1%
o 19
 
0.8%
Other values (33) 140
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 263
11.0%
259
10.9%
l 255
10.7%
e 247
10.4%
S 241
10.1%
p 240
10.1%
m 240
10.1%
O 240
10.1%
Y 240
10.1%
o 19
 
0.8%
Other values (33) 140
5.9%

eventDate
Text

Missing 

Distinct100750
Distinct (%)2.5%
Missing499670
Missing (%)11.1%
Memory size34.5 MiB
2025-03-26T16:24:56.309095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length10
Mean length10.22909118
Min length4

Characters and Unicode

Total characters41095650
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26184 ?
Unique (%)0.7%

Sample

1st row1981-04-30
2nd row1954-08-07
3rd row1947-04-03
4th row1966-04-01
5th row1971-03-23
ValueCountFrequency (%)
or 7515
 
0.2%
1838/1842 7002
 
0.2%
1891 4649
 
0.1%
1760/1808 3659
 
0.1%
1889 3487
 
0.1%
1875 3341
 
0.1%
1853/1856 3326
 
0.1%
1890 3161
 
0.1%
1923 3140
 
0.1%
1887 3048
 
0.1%
Other values (96845) 3990229
99.0%
2025-03-26T16:24:56.532303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7804169
19.0%
- 7729324
18.8%
0 6240333
15.2%
9 5177988
12.6%
2 3078621
 
7.5%
8 2444395
 
5.9%
7 1744923
 
4.2%
6 1741907
 
4.2%
3 1670851
 
4.1%
5 1553407
 
3.8%
Other values (6) 1909732
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41095650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7804169
19.0%
- 7729324
18.8%
0 6240333
15.2%
9 5177988
12.6%
2 3078621
 
7.5%
8 2444395
 
5.9%
7 1744923
 
4.2%
6 1741907
 
4.2%
3 1670851
 
4.1%
5 1553407
 
3.8%
Other values (6) 1909732
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41095650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7804169
19.0%
- 7729324
18.8%
0 6240333
15.2%
9 5177988
12.6%
2 3078621
 
7.5%
8 2444395
 
5.9%
7 1744923
 
4.2%
6 1741907
 
4.2%
3 1670851
 
4.1%
5 1553407
 
3.8%
Other values (6) 1909732
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41095650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7804169
19.0%
- 7729324
18.8%
0 6240333
15.2%
9 5177988
12.6%
2 3078621
 
7.5%
8 2444395
 
5.9%
7 1744923
 
4.2%
6 1741907
 
4.2%
3 1670851
 
4.1%
5 1553407
 
3.8%
Other values (6) 1909732
 
4.6%

startDayOfYear
Text

Missing 

Distinct366
Distinct (%)< 0.1%
Missing708176
Missing (%)15.7%
Memory size34.5 MiB
2025-03-26T16:24:56.676102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.786182066
Min length1

Characters and Unicode

Total characters10612626
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row219
3rd row93
4th row91
5th row82
ValueCountFrequency (%)
212 65271
 
1.7%
243 53944
 
1.4%
181 53269
 
1.4%
151 48922
 
1.3%
120 37900
 
1.0%
213 35194
 
0.9%
273 34764
 
0.9%
90 31613
 
0.8%
304 30722
 
0.8%
244 28692
 
0.8%
Other values (356) 3388730
89.0%
2025-03-26T16:24:56.875305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2142572
20.2%
2 2136325
20.1%
3 1271497
12.0%
4 814963
 
7.7%
5 784480
 
7.4%
0 747407
 
7.0%
9 692263
 
6.5%
6 686231
 
6.5%
8 680238
 
6.4%
7 656650
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10612626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2142572
20.2%
2 2136325
20.1%
3 1271497
12.0%
4 814963
 
7.7%
5 784480
 
7.4%
0 747407
 
7.0%
9 692263
 
6.5%
6 686231
 
6.5%
8 680238
 
6.4%
7 656650
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10612626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2142572
20.2%
2 2136325
20.1%
3 1271497
12.0%
4 814963
 
7.7%
5 784480
 
7.4%
0 747407
 
7.0%
9 692263
 
6.5%
6 686231
 
6.5%
8 680238
 
6.4%
7 656650
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10612626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2142572
20.2%
2 2136325
20.1%
3 1271497
12.0%
4 814963
 
7.7%
5 784480
 
7.4%
0 747407
 
7.0%
9 692263
 
6.5%
6 686231
 
6.5%
8 680238
 
6.4%
7 656650
 
6.2%

endDayOfYear
Text

Missing 

Distinct366
Distinct (%)< 0.1%
Missing706537
Missing (%)15.6%
Memory size34.5 MiB
2025-03-26T16:24:57.012267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.78732713
Min length1

Characters and Unicode

Total characters10621556
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row219
3rd row93
4th row91
5th row82
ValueCountFrequency (%)
212 66140
 
1.7%
243 57290
 
1.5%
181 53228
 
1.4%
151 44304
 
1.2%
120 37748
 
1.0%
273 36155
 
0.9%
90 33366
 
0.9%
304 33033
 
0.9%
213 32577
 
0.9%
244 30646
 
0.8%
Other values (356) 3386173
88.9%
2025-03-26T16:24:57.203480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2141580
20.2%
1 2116710
19.9%
3 1288747
12.1%
4 827202
 
7.8%
5 780295
 
7.3%
0 750054
 
7.1%
9 689280
 
6.5%
6 683660
 
6.4%
8 682103
 
6.4%
7 661925
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10621556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2141580
20.2%
1 2116710
19.9%
3 1288747
12.1%
4 827202
 
7.8%
5 780295
 
7.3%
0 750054
 
7.1%
9 689280
 
6.5%
6 683660
 
6.4%
8 682103
 
6.4%
7 661925
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10621556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2141580
20.2%
1 2116710
19.9%
3 1288747
12.1%
4 827202
 
7.8%
5 780295
 
7.3%
0 750054
 
7.1%
9 689280
 
6.5%
6 683660
 
6.4%
8 682103
 
6.4%
7 661925
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10621556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2141580
20.2%
1 2116710
19.9%
3 1288747
12.1%
4 827202
 
7.8%
5 780295
 
7.3%
0 750054
 
7.1%
9 689280
 
6.5%
6 683660
 
6.4%
8 682103
 
6.4%
7 661925
 
6.2%

year
Text

Missing 

Distinct275
Distinct (%)< 0.1%
Missing499670
Missing (%)11.1%
Memory size34.5 MiB
2025-03-26T16:24:57.343817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16070108
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row1981
2nd row1954
3rd row1947
4th row1966
5th row1971
ValueCountFrequency (%)
1966 52763
 
1.3%
1964 51520
 
1.3%
1939 48559
 
1.2%
1949 46755
 
1.2%
1929 45892
 
1.1%
1938 45011
 
1.1%
1965 44913
 
1.1%
1922 42910
 
1.1%
1962 42345
 
1.1%
1968 41363
 
1.0%
Other values (265) 3555496
88.5%
2025-03-26T16:24:57.537938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4565783
28.4%
9 4114858
25.6%
8 1419843
 
8.8%
0 1047796
 
6.5%
2 965104
 
6.0%
6 887167
 
5.5%
4 787580
 
4.9%
3 777249
 
4.8%
7 755777
 
4.7%
5 748951
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16070108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4565783
28.4%
9 4114858
25.6%
8 1419843
 
8.8%
0 1047796
 
6.5%
2 965104
 
6.0%
6 887167
 
5.5%
4 787580
 
4.9%
3 777249
 
4.8%
7 755777
 
4.7%
5 748951
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16070108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4565783
28.4%
9 4114858
25.6%
8 1419843
 
8.8%
0 1047796
 
6.5%
2 965104
 
6.0%
6 887167
 
5.5%
4 787580
 
4.9%
3 777249
 
4.8%
7 755777
 
4.7%
5 748951
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16070108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4565783
28.4%
9 4114858
25.6%
8 1419843
 
8.8%
0 1047796
 
6.5%
2 965104
 
6.0%
6 887167
 
5.5%
4 787580
 
4.9%
3 777249
 
4.8%
7 755777
 
4.7%
5 748951
 
4.7%

month
Text

Missing 

Distinct12
Distinct (%)< 0.1%
Missing700285
Missing (%)15.5%
Memory size34.5 MiB
2025-03-26T16:24:57.588939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.171103237
Min length1

Characters and Unicode

Total characters4469998
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row8
3rd row4
4th row4
5th row3
ValueCountFrequency (%)
7 547453
14.3%
8 491796
12.9%
6 411493
10.8%
5 354317
9.3%
9 340815
8.9%
4 293818
7.7%
3 268110
7.0%
10 260814
6.8%
2 235592
6.2%
1 220432
5.8%
Other values (2) 392272
10.3%
2025-03-26T16:24:57.675535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1081034
24.2%
7 547453
12.2%
8 491796
11.0%
2 420348
 
9.4%
6 411493
 
9.2%
5 354317
 
7.9%
9 340815
 
7.6%
4 293818
 
6.6%
3 268110
 
6.0%
0 260814
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4469998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1081034
24.2%
7 547453
12.2%
8 491796
11.0%
2 420348
 
9.4%
6 411493
 
9.2%
5 354317
 
7.9%
9 340815
 
7.6%
4 293818
 
6.6%
3 268110
 
6.0%
0 260814
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4469998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1081034
24.2%
7 547453
12.2%
8 491796
11.0%
2 420348
 
9.4%
6 411493
 
9.2%
5 354317
 
7.9%
9 340815
 
7.6%
4 293818
 
6.6%
3 268110
 
6.0%
0 260814
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4469998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1081034
24.2%
7 547453
12.2%
8 491796
11.0%
2 420348
 
9.4%
6 411493
 
9.2%
5 354317
 
7.9%
9 340815
 
7.6%
4 293818
 
6.6%
3 268110
 
6.0%
0 260814
 
5.8%

day
Text

Missing 

Distinct31
Distinct (%)< 0.1%
Missing1180412
Missing (%)26.1%
Memory size34.5 MiB
2025-03-26T16:24:57.716097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.709503909
Min length1

Characters and Unicode

Total characters5704247
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row7
3rd row3
4th row1
5th row23
ValueCountFrequency (%)
20 124858
 
3.7%
15 121566
 
3.6%
10 116432
 
3.5%
1 116323
 
3.5%
18 114568
 
3.4%
25 112486
 
3.4%
19 112112
 
3.4%
17 111778
 
3.3%
12 111074
 
3.3%
8 109982
 
3.3%
Other values (21) 2185606
65.5%
2025-03-26T16:24:57.811571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1506975
26.4%
2 1420255
24.9%
3 471507
 
8.3%
5 342799
 
6.0%
0 340257
 
6.0%
8 333524
 
5.8%
7 328457
 
5.8%
6 325372
 
5.7%
4 321775
 
5.6%
9 313326
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5704247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1506975
26.4%
2 1420255
24.9%
3 471507
 
8.3%
5 342799
 
6.0%
0 340257
 
6.0%
8 333524
 
5.8%
7 328457
 
5.8%
6 325372
 
5.7%
4 321775
 
5.6%
9 313326
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5704247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1506975
26.4%
2 1420255
24.9%
3 471507
 
8.3%
5 342799
 
6.0%
0 340257
 
6.0%
8 333524
 
5.8%
7 328457
 
5.8%
6 325372
 
5.7%
4 321775
 
5.6%
9 313326
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5704247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1506975
26.4%
2 1420255
24.9%
3 471507
 
8.3%
5 342799
 
6.0%
0 340257
 
6.0%
8 333524
 
5.8%
7 328457
 
5.8%
6 325372
 
5.7%
4 321775
 
5.6%
9 313326
 
5.5%

verbatimEventDate
Text

Missing 

Distinct144626
Distinct (%)9.5%
Missing2996092
Missing (%)66.3%
Memory size34.5 MiB
2025-03-26T16:24:57.962651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length103
Median length11
Mean length13.38676488
Min length1

Characters and Unicode

Total characters20362675
Distinct characters101
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47167 ?
Unique (%)3.1%

Sample

1st row30 Apr 1981
2nd row16 Dec 1953
3rd row-- --- ----
4th row01 Feb 1974
5th rowTranscribed d/m/y: 28/4/76
ValueCountFrequency (%)
569812
 
12.1%
transcribed 163851
 
3.5%
d/m/y 163850
 
3.5%
jul 133554
 
2.8%
aug 127356
 
2.7%
may 100730
 
2.1%
sep 100431
 
2.1%
jun 99947
 
2.1%
mar 89944
 
1.9%
to 89391
 
1.9%
Other values (49771) 3066788
65.2%
2025-03-26T16:24:58.179303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3184549
15.6%
1 2057594
 
10.1%
- 1706512
 
8.4%
9 1491481
 
7.3%
2 903034
 
4.4%
0 759635
 
3.7%
/ 668317
 
3.3%
8 664932
 
3.3%
r 587069
 
2.9%
e 491003
 
2.4%
Other values (91) 7848549
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20362675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3184549
15.6%
1 2057594
 
10.1%
- 1706512
 
8.4%
9 1491481
 
7.3%
2 903034
 
4.4%
0 759635
 
3.7%
/ 668317
 
3.3%
8 664932
 
3.3%
r 587069
 
2.9%
e 491003
 
2.4%
Other values (91) 7848549
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20362675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3184549
15.6%
1 2057594
 
10.1%
- 1706512
 
8.4%
9 1491481
 
7.3%
2 903034
 
4.4%
0 759635
 
3.7%
/ 668317
 
3.3%
8 664932
 
3.3%
r 587069
 
2.9%
e 491003
 
2.4%
Other values (91) 7848549
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20362675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3184549
15.6%
1 2057594
 
10.1%
- 1706512
 
8.4%
9 1491481
 
7.3%
2 903034
 
4.4%
0 759635
 
3.7%
/ 668317
 
3.3%
8 664932
 
3.3%
r 587069
 
2.9%
e 491003
 
2.4%
Other values (91) 7848549
38.5%

habitat
Text

Missing 

Distinct179386
Distinct (%)35.4%
Missing4010721
Missing (%)88.8%
Memory size34.5 MiB
2025-03-26T16:24:58.325847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length837
Median length446
Mean length33.42569835
Min length1

Characters and Unicode

Total characters16929314
Distinct characters139
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136952 ?
Unique (%)27.0%

Sample

1st rowErect.
2nd rowPlanted
3rd rowHillsides covered with broad-leaved forest, understory with Arthrostylidium, Rubus, and numerous ferns, epiphytes and Usnea.
4th rowOpen to closed forest with Pinus contorta, Populus tremuloides, Purshia tridentata, and Ribes cereum.
5th rowDeep secondary forest; clay soil
ValueCountFrequency (%)
forest 131277
 
5.0%
on 90050
 
3.5%
and 73886
 
2.8%
in 67435
 
2.6%
with 54119
 
2.1%
of 49682
 
1.9%
along 29257
 
1.1%
de 27728
 
1.1%
soil 24650
 
0.9%
slopes 22336
 
0.9%
Other values (41721) 2032143
78.1%
2025-03-26T16:24:58.542046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2096087
12.4%
e 1532651
 
9.1%
a 1340240
 
7.9%
o 1221746
 
7.2%
r 1065983
 
6.3%
s 1063191
 
6.3%
n 1047150
 
6.2%
i 896102
 
5.3%
t 848298
 
5.0%
l 662238
 
3.9%
Other values (129) 5155628
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16929314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2096087
12.4%
e 1532651
 
9.1%
a 1340240
 
7.9%
o 1221746
 
7.2%
r 1065983
 
6.3%
s 1063191
 
6.3%
n 1047150
 
6.2%
i 896102
 
5.3%
t 848298
 
5.0%
l 662238
 
3.9%
Other values (129) 5155628
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16929314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2096087
12.4%
e 1532651
 
9.1%
a 1340240
 
7.9%
o 1221746
 
7.2%
r 1065983
 
6.3%
s 1063191
 
6.3%
n 1047150
 
6.2%
i 896102
 
5.3%
t 848298
 
5.0%
l 662238
 
3.9%
Other values (129) 5155628
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16929314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2096087
12.4%
e 1532651
 
9.1%
a 1340240
 
7.9%
o 1221746
 
7.2%
r 1065983
 
6.3%
s 1063191
 
6.3%
n 1047150
 
6.2%
i 896102
 
5.3%
t 848298
 
5.0%
l 662238
 
3.9%
Other values (129) 5155628
30.5%

locationID
Text

Missing 

Distinct1109
Distinct (%)2.7%
Missing4475514
Missing (%)99.1%
Memory size34.5 MiB
2025-03-26T16:24:58.677373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length37
Median length5
Mean length5.996113523
Min length1

Characters and Unicode

Total characters249936
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique363 ?
Unique (%)0.9%

Sample

1st row66-10
2nd row69-11
3rd row64-51
4th row66-14
5th row64-34
ValueCountFrequency (%)
station 4950
 
10.3%
ms04 1735
 
3.6%
66-24 1381
 
2.9%
61 947
 
2.0%
64-48 628
 
1.3%
64-47 588
 
1.2%
69-14 562
 
1.2%
66-39 462
 
1.0%
66-28 442
 
0.9%
66-17 426
 
0.9%
Other values (1008) 36079
74.9%
2025-03-26T16:24:58.877659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 43829
17.5%
- 36334
14.5%
4 21704
 
8.7%
2 20173
 
8.1%
1 18273
 
7.3%
0 15413
 
6.2%
3 11189
 
4.5%
7 10466
 
4.2%
t 10136
 
4.1%
8 7707
 
3.1%
Other values (63) 54712
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 249936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 43829
17.5%
- 36334
14.5%
4 21704
 
8.7%
2 20173
 
8.1%
1 18273
 
7.3%
0 15413
 
6.2%
3 11189
 
4.5%
7 10466
 
4.2%
t 10136
 
4.1%
8 7707
 
3.1%
Other values (63) 54712
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 249936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 43829
17.5%
- 36334
14.5%
4 21704
 
8.7%
2 20173
 
8.1%
1 18273
 
7.3%
0 15413
 
6.2%
3 11189
 
4.5%
7 10466
 
4.2%
t 10136
 
4.1%
8 7707
 
3.1%
Other values (63) 54712
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 249936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 43829
17.5%
- 36334
14.5%
4 21704
 
8.7%
2 20173
 
8.1%
1 18273
 
7.3%
0 15413
 
6.2%
3 11189
 
4.5%
7 10466
 
4.2%
t 10136
 
4.1%
8 7707
 
3.1%
Other values (63) 54712
21.9%
Distinct29231
Distinct (%)0.7%
Missing38633
Missing (%)0.9%
Memory size34.5 MiB
2025-03-26T16:24:59.014975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length118
Mean length40.94728511
Min length4

Characters and Unicode

Total characters183385037
Distinct characters163
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9053 ?
Unique (%)0.2%

Sample

1st rowNorth America, United States, Florida
2nd rowSouth America - Neotropics, Peru, Piura
3rd rowSouth America, Argentina, Formosa
4th rowSouth America - Neotropics, Venezuela, Carabobo
5th rowAfrica, South Africa
ValueCountFrequency (%)
america 3038893
 
12.5%
north 1751786
 
7.2%
1669173
 
6.8%
neotropics 1605388
 
6.6%
united 1352124
 
5.5%
states 1342960
 
5.5%
south 1162353
 
4.8%
mexico 330302
 
1.4%
asia-tropical 304048
 
1.2%
brazil 300018
 
1.2%
Other values (15123) 11520053
47.3%
2025-03-26T16:24:59.231999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19898534
 
10.9%
a 16908084
 
9.2%
i 13663986
 
7.5%
e 13431607
 
7.3%
r 11625539
 
6.3%
t 11457349
 
6.2%
o 11173033
 
6.1%
, 9365081
 
5.1%
n 7222156
 
3.9%
c 7116210
 
3.9%
Other values (153) 61523458
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183385037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19898534
 
10.9%
a 16908084
 
9.2%
i 13663986
 
7.5%
e 13431607
 
7.3%
r 11625539
 
6.3%
t 11457349
 
6.2%
o 11173033
 
6.1%
, 9365081
 
5.1%
n 7222156
 
3.9%
c 7116210
 
3.9%
Other values (153) 61523458
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183385037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19898534
 
10.9%
a 16908084
 
9.2%
i 13663986
 
7.5%
e 13431607
 
7.3%
r 11625539
 
6.3%
t 11457349
 
6.2%
o 11173033
 
6.1%
, 9365081
 
5.1%
n 7222156
 
3.9%
c 7116210
 
3.9%
Other values (153) 61523458
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183385037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19898534
 
10.9%
a 16908084
 
9.2%
i 13663986
 
7.5%
e 13431607
 
7.3%
r 11625539
 
6.3%
t 11457349
 
6.2%
o 11173033
 
6.1%
, 9365081
 
5.1%
n 7222156
 
3.9%
c 7116210
 
3.9%
Other values (153) 61523458
33.5%

continent
Text

Missing 

Distinct72
Distinct (%)< 0.1%
Missing66179
Missing (%)1.5%
Memory size34.5 MiB
2025-03-26T16:24:59.275996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length57
Median length50
Mean length17.22840258
Min length4

Characters and Unicode

Total characters76683930
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowNorth America
2nd rowSouth America - Neotropics
3rd rowSouth America
4th rowSouth America - Neotropics
5th rowAfrica
ValueCountFrequency (%)
america 3038892
27.2%
north 1705875
15.3%
1606504
14.4%
neotropics 1605388
14.4%
south 1078634
 
9.7%
asia-tropical 304048
 
2.7%
central 269908
 
2.4%
west 265276
 
2.4%
indies 265276
 
2.4%
europe 231038
 
2.1%
Other values (19) 797215
 
7.1%
2025-03-26T16:24:59.373857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 7610369
 
9.9%
6717036
 
8.8%
o 6534904
 
8.5%
e 6339075
 
8.3%
i 6295445
 
8.2%
c 5459332
 
7.1%
t 5252183
 
6.8%
a 5074035
 
6.6%
A 3812871
 
5.0%
N 3311261
 
4.3%
Other values (30) 20277419
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76683930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 7610369
 
9.9%
6717036
 
8.8%
o 6534904
 
8.5%
e 6339075
 
8.3%
i 6295445
 
8.2%
c 5459332
 
7.1%
t 5252183
 
6.8%
a 5074035
 
6.6%
A 3812871
 
5.0%
N 3311261
 
4.3%
Other values (30) 20277419
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76683930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 7610369
 
9.9%
6717036
 
8.8%
o 6534904
 
8.5%
e 6339075
 
8.3%
i 6295445
 
8.2%
c 5459332
 
7.1%
t 5252183
 
6.8%
a 5074035
 
6.6%
A 3812871
 
5.0%
N 3311261
 
4.3%
Other values (30) 20277419
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76683930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 7610369
 
9.9%
6717036
 
8.8%
o 6534904
 
8.5%
e 6339075
 
8.3%
i 6295445
 
8.2%
c 5459332
 
7.1%
t 5252183
 
6.8%
a 5074035
 
6.6%
A 3812871
 
5.0%
N 3311261
 
4.3%
Other values (30) 20277419
26.4%

waterBody
Text

Missing 

Distinct147
Distinct (%)0.8%
Missing4497616
Missing (%)99.6%
Memory size34.5 MiB
2025-03-26T16:24:59.408860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length62
Median length61
Mean length26.33629539
Min length4

Characters and Unicode

Total characters515691
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.3%

Sample

1st rowNorth Atlantic Ocean, Bay of Fundy
2nd rowNorth Atlantic Ocean, Caribbean Sea
3rd rowNorth Atlantic Ocean, Gulf of Maine, Englishman Bay/Mack Cove
4th rowNorth Atlantic Ocean, Caribbean Sea
5th rowNorth Pacific Ocean
ValueCountFrequency (%)
ocean 15746
19.7%
north 15152
19.0%
atlantic 14299
17.9%
sea 7189
9.0%
caribbean 6009
 
7.5%
of 3766
 
4.7%
gulf 3585
 
4.5%
maine 2789
 
3.5%
bay 2527
 
3.2%
pacific 1232
 
1.5%
Other values (154) 7587
9.5%
2025-03-26T16:24:59.510613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60300
11.7%
a 59605
11.6%
t 46446
 
9.0%
n 42154
 
8.2%
e 36559
 
7.1%
c 34235
 
6.6%
i 27768
 
5.4%
r 23441
 
4.5%
o 23405
 
4.5%
l 19306
 
3.7%
Other values (48) 142472
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 515691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60300
11.7%
a 59605
11.6%
t 46446
 
9.0%
n 42154
 
8.2%
e 36559
 
7.1%
c 34235
 
6.6%
i 27768
 
5.4%
r 23441
 
4.5%
o 23405
 
4.5%
l 19306
 
3.7%
Other values (48) 142472
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 515691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60300
11.7%
a 59605
11.6%
t 46446
 
9.0%
n 42154
 
8.2%
e 36559
 
7.1%
c 34235
 
6.6%
i 27768
 
5.4%
r 23441
 
4.5%
o 23405
 
4.5%
l 19306
 
3.7%
Other values (48) 142472
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 515691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60300
11.7%
a 59605
11.6%
t 46446
 
9.0%
n 42154
 
8.2%
e 36559
 
7.1%
c 34235
 
6.6%
i 27768
 
5.4%
r 23441
 
4.5%
o 23405
 
4.5%
l 19306
 
3.7%
Other values (48) 142472
27.6%

islandGroup
Text

Missing 

Distinct535
Distinct (%)0.5%
Missing4404567
Missing (%)97.5%
Memory size34.5 MiB
2025-03-26T16:24:59.636847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length41
Mean length14.86260321
Min length3

Characters and Unicode

Total characters1673975
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.1%

Sample

1st rowGreater Antilles
2nd rowGreater Antilles
3rd rowElizabeth Islands
4th rowChannel Islands
5th rowGreater Antilles
ValueCountFrequency (%)
antilles 32234
 
12.5%
greater 32231
 
12.5%
islands 23351
 
9.1%
is 19924
 
7.7%
group 16207
 
6.3%
new 7380
 
2.9%
guinea 6019
 
2.3%
channel 5396
 
2.1%
keys 5337
 
2.1%
florida 5076
 
2.0%
Other values (442) 104094
40.5%
2025-03-26T16:24:59.835216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 168669
 
10.1%
a 153530
 
9.2%
144619
 
8.6%
s 133177
 
8.0%
l 127555
 
7.6%
r 120110
 
7.2%
n 113363
 
6.8%
t 87901
 
5.3%
i 82805
 
4.9%
G 60282
 
3.6%
Other values (54) 481964
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1673975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 168669
 
10.1%
a 153530
 
9.2%
144619
 
8.6%
s 133177
 
8.0%
l 127555
 
7.6%
r 120110
 
7.2%
n 113363
 
6.8%
t 87901
 
5.3%
i 82805
 
4.9%
G 60282
 
3.6%
Other values (54) 481964
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1673975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 168669
 
10.1%
a 153530
 
9.2%
144619
 
8.6%
s 133177
 
8.0%
l 127555
 
7.6%
r 120110
 
7.2%
n 113363
 
6.8%
t 87901
 
5.3%
i 82805
 
4.9%
G 60282
 
3.6%
Other values (54) 481964
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1673975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 168669
 
10.1%
a 153530
 
9.2%
144619
 
8.6%
s 133177
 
8.0%
l 127555
 
7.6%
r 120110
 
7.2%
n 113363
 
6.8%
t 87901
 
5.3%
i 82805
 
4.9%
G 60282
 
3.6%
Other values (54) 481964
28.8%

island
Text

Missing 

Distinct4295
Distinct (%)1.1%
Missing4140568
Missing (%)91.7%
Memory size34.5 MiB
2025-03-26T16:24:59.965017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length43
Mean length9.545709438
Min length1

Characters and Unicode

Total characters3595191
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1310 ?
Unique (%)0.3%

Sample

1st rowRota
2nd rowHispaniola
3rd rowNorth Island
4th rowKaua'i
5th rowHispaniola Island
ValueCountFrequency (%)
hispaniola 49175
 
8.5%
island 45119
 
7.8%
cuba 23051
 
4.0%
oahu 17044
 
2.9%
st 12453
 
2.2%
kaua'i 12005
 
2.1%
new 10491
 
1.8%
isla 9890
 
1.7%
jamaica 9867
 
1.7%
luzon 9658
 
1.7%
Other values (3257) 379489
65.6%
2025-03-26T16:25:00.173244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 573060
15.9%
i 282997
 
7.9%
n 241832
 
6.7%
o 215365
 
6.0%
201613
 
5.6%
l 189281
 
5.3%
u 174446
 
4.9%
e 170884
 
4.8%
s 161593
 
4.5%
r 127330
 
3.5%
Other values (77) 1256790
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3595191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 573060
15.9%
i 282997
 
7.9%
n 241832
 
6.7%
o 215365
 
6.0%
201613
 
5.6%
l 189281
 
5.3%
u 174446
 
4.9%
e 170884
 
4.8%
s 161593
 
4.5%
r 127330
 
3.5%
Other values (77) 1256790
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3595191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 573060
15.9%
i 282997
 
7.9%
n 241832
 
6.7%
o 215365
 
6.0%
201613
 
5.6%
l 189281
 
5.3%
u 174446
 
4.9%
e 170884
 
4.8%
s 161593
 
4.5%
r 127330
 
3.5%
Other values (77) 1256790
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3595191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 573060
15.9%
i 282997
 
7.9%
n 241832
 
6.7%
o 215365
 
6.0%
201613
 
5.6%
l 189281
 
5.3%
u 174446
 
4.9%
e 170884
 
4.8%
s 161593
 
4.5%
r 127330
 
3.5%
Other values (77) 1256790
35.0%
Distinct460
Distinct (%)< 0.1%
Missing38689
Missing (%)0.9%
Memory size34.5 MiB
2025-03-26T16:25:00.311537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length50
Mean length9.388815204
Min length4

Characters and Unicode

Total characters42047884
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowPeru
3rd rowArgentina
4th rowVenezuela
5th rowSouth Africa
ValueCountFrequency (%)
united 1352120
21.1%
states 1342960
21.0%
brazil 300018
 
4.7%
mexico 290299
 
4.5%
colombia 165099
 
2.6%
venezuela 119736
 
1.9%
peru 116308
 
1.8%
canada 113214
 
1.8%
china 108398
 
1.7%
ecuador 89326
 
1.4%
Other values (309) 2404080
37.6%
2025-03-26T16:25:00.509954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5088168
12.1%
t 4554311
 
10.8%
e 4468467
 
10.6%
i 3790720
 
9.0%
n 3121640
 
7.4%
d 1944287
 
4.6%
1923050
 
4.6%
s 1852474
 
4.4%
S 1538945
 
3.7%
U 1422007
 
3.4%
Other values (55) 12343815
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42047884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5088168
12.1%
t 4554311
 
10.8%
e 4468467
 
10.6%
i 3790720
 
9.0%
n 3121640
 
7.4%
d 1944287
 
4.6%
1923050
 
4.6%
s 1852474
 
4.4%
S 1538945
 
3.7%
U 1422007
 
3.4%
Other values (55) 12343815
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42047884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5088168
12.1%
t 4554311
 
10.8%
e 4468467
 
10.6%
i 3790720
 
9.0%
n 3121640
 
7.4%
d 1944287
 
4.6%
1923050
 
4.6%
s 1852474
 
4.4%
S 1538945
 
3.7%
U 1422007
 
3.4%
Other values (55) 12343815
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42047884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5088168
12.1%
t 4554311
 
10.8%
e 4468467
 
10.6%
i 3790720
 
9.0%
n 3121640
 
7.4%
d 1944287
 
4.6%
1923050
 
4.6%
s 1852474
 
4.4%
S 1538945
 
3.7%
U 1422007
 
3.4%
Other values (55) 12343815
29.4%

stateProvince
Text

Missing 

Distinct4563
Distinct (%)0.1%
Missing1002532
Missing (%)22.2%
Memory size34.5 MiB
2025-03-26T16:25:00.651412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length51
Mean length9.008144446
Min length1

Characters and Unicode

Total characters31660610
Distinct characters144
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1056 ?
Unique (%)< 0.1%

Sample

1st rowFlorida
2nd rowPiura
3rd rowFormosa
4th rowCarabobo
5th rowManabí
ValueCountFrequency (%)
california 202064
 
4.4%
new 105446
 
2.3%
florida 88769
 
1.9%
virginia 72255
 
1.6%
texas 71253
 
1.5%
alaska 67224
 
1.5%
amazonas 60940
 
1.3%
hawaii 55261
 
1.2%
san 50594
 
1.1%
arizona 50410
 
1.1%
Other values (3812) 3800037
82.2%
2025-03-26T16:25:00.866657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4913397
15.5%
i 2592329
 
8.2%
n 2328404
 
7.4%
o 2313033
 
7.3%
r 2011993
 
6.4%
e 1597027
 
5.0%
s 1274561
 
4.0%
l 1255070
 
4.0%
t 1113259
 
3.5%
1109588
 
3.5%
Other values (134) 11151949
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31660610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4913397
15.5%
i 2592329
 
8.2%
n 2328404
 
7.4%
o 2313033
 
7.3%
r 2011993
 
6.4%
e 1597027
 
5.0%
s 1274561
 
4.0%
l 1255070
 
4.0%
t 1113259
 
3.5%
1109588
 
3.5%
Other values (134) 11151949
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31660610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4913397
15.5%
i 2592329
 
8.2%
n 2328404
 
7.4%
o 2313033
 
7.3%
r 2011993
 
6.4%
e 1597027
 
5.0%
s 1274561
 
4.0%
l 1255070
 
4.0%
t 1113259
 
3.5%
1109588
 
3.5%
Other values (134) 11151949
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31660610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4913397
15.5%
i 2592329
 
8.2%
n 2328404
 
7.4%
o 2313033
 
7.3%
r 2011993
 
6.4%
e 1597027
 
5.0%
s 1274561
 
4.0%
l 1255070
 
4.0%
t 1113259
 
3.5%
1109588
 
3.5%
Other values (134) 11151949
35.2%

county
Text

Missing 

Distinct12277
Distinct (%)1.7%
Missing3779982
Missing (%)83.7%
Memory size34.5 MiB
2025-03-26T16:25:01.010851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length49
Mean length9.153378594
Min length1

Characters and Unicode

Total characters6748008
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3590 ?
Unique (%)0.5%

Sample

1st rowParroquia
2nd rowDuval
3rd rowBoulder
4th rowCantal
5th rowArlington
ValueCountFrequency (%)
county 55951
 
5.4%
san 32863
 
3.2%
prince 19205
 
1.9%
honolulu 19186
 
1.8%
santa 18078
 
1.7%
los 14018
 
1.4%
angeles 13833
 
1.3%
montgomery 13796
 
1.3%
george's 13725
 
1.3%
maui 12918
 
1.2%
Other values (9383) 823763
79.4%
2025-03-26T16:25:01.212898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 769668
 
11.4%
o 546564
 
8.1%
n 534097
 
7.9%
e 516007
 
7.6%
r 422604
 
6.3%
i 392670
 
5.8%
t 309472
 
4.6%
u 307237
 
4.6%
300121
 
4.4%
l 289759
 
4.3%
Other values (101) 2359809
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6748008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 769668
 
11.4%
o 546564
 
8.1%
n 534097
 
7.9%
e 516007
 
7.6%
r 422604
 
6.3%
i 392670
 
5.8%
t 309472
 
4.6%
u 307237
 
4.6%
300121
 
4.4%
l 289759
 
4.3%
Other values (101) 2359809
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6748008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 769668
 
11.4%
o 546564
 
8.1%
n 534097
 
7.9%
e 516007
 
7.6%
r 422604
 
6.3%
i 392670
 
5.8%
t 309472
 
4.6%
u 307237
 
4.6%
300121
 
4.4%
l 289759
 
4.3%
Other values (101) 2359809
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6748008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 769668
 
11.4%
o 546564
 
8.1%
n 534097
 
7.9%
e 516007
 
7.6%
r 422604
 
6.3%
i 392670
 
5.8%
t 309472
 
4.6%
u 307237
 
4.6%
300121
 
4.4%
l 289759
 
4.3%
Other values (101) 2359809
35.0%

locality
Text

Missing 

Distinct2191006
Distinct (%)52.4%
Missing332140
Missing (%)7.4%
Memory size34.5 MiB
2025-03-26T16:25:01.641549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1122
Median length399
Mean length47.28496171
Min length1

Characters and Unicode

Total characters197890260
Distinct characters422
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1771688 ?
Unique (%)42.3%

Sample

1st rowGulf of Mexico
2nd rowDept. Piura: Ayabaca
3rd rowDep. Pilcomayo. al E a 2 Km de P. Porteño.
4th rowSelva siempre verde en las quebradas al norte de Los Tanques, arriba de la Planta Eléctrica, en las cabeceras del Río San Gián, al sur de Borburata.
5th rowFlat terrain near Skukuza rest camp, Kruger National Park.
ValueCountFrequency (%)
of 1585460
 
5.0%
de 609580
 
1.9%
the 376970
 
1.2%
km 372163
 
1.2%
near 341343
 
1.1%
and 273707
 
0.9%
on 273642
 
0.9%
in 261695
 
0.8%
county 254729
 
0.8%
la 230304
 
0.7%
Other values (590176) 26833979
85.4%
2025-03-26T16:25:02.179971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27228388
 
13.8%
a 18121869
 
9.2%
e 13978124
 
7.1%
o 13187183
 
6.7%
n 11000177
 
5.6%
i 10193120
 
5.2%
r 10094081
 
5.1%
t 8763492
 
4.4%
l 7086889
 
3.6%
s 6886060
 
3.5%
Other values (412) 71350877
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197890260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27228388
 
13.8%
a 18121869
 
9.2%
e 13978124
 
7.1%
o 13187183
 
6.7%
n 11000177
 
5.6%
i 10193120
 
5.2%
r 10094081
 
5.1%
t 8763492
 
4.4%
l 7086889
 
3.6%
s 6886060
 
3.5%
Other values (412) 71350877
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197890260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27228388
 
13.8%
a 18121869
 
9.2%
e 13978124
 
7.1%
o 13187183
 
6.7%
n 11000177
 
5.6%
i 10193120
 
5.2%
r 10094081
 
5.1%
t 8763492
 
4.4%
l 7086889
 
3.6%
s 6886060
 
3.5%
Other values (412) 71350877
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197890260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27228388
 
13.8%
a 18121869
 
9.2%
e 13978124
 
7.1%
o 13187183
 
6.7%
n 11000177
 
5.6%
i 10193120
 
5.2%
r 10094081
 
5.1%
t 8763492
 
4.4%
l 7086889
 
3.6%
s 6886060
 
3.5%
Other values (412) 71350877
36.1%
Distinct4969
Distinct (%)0.3%
Missing2861975
Missing (%)63.4%
Memory size34.5 MiB
2025-03-26T16:25:02.319069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.339254795
Min length3

Characters and Unicode

Total characters8837652
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique772 ?
Unique (%)< 0.1%

Sample

1st row2742.0
2nd row750.0
3rd row50.0
4th row0.0
5th row17.0
ValueCountFrequency (%)
100.0 34126
 
2.1%
1000.0 33141
 
2.0%
200.0 29739
 
1.8%
300.0 26715
 
1.6%
500.0 26616
 
1.6%
1500.0 25898
 
1.6%
800.0 25545
 
1.5%
400.0 23974
 
1.4%
900.0 23549
 
1.4%
1200.0 23154
 
1.4%
Other values (4929) 1382765
83.5%
2025-03-26T16:25:02.519635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3505145
39.7%
. 1655222
18.7%
1 807187
 
9.1%
2 628069
 
7.1%
5 506641
 
5.7%
3 406625
 
4.6%
4 319061
 
3.6%
6 271718
 
3.1%
8 261479
 
3.0%
7 254224
 
2.9%
Other values (2) 222281
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8837652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3505145
39.7%
. 1655222
18.7%
1 807187
 
9.1%
2 628069
 
7.1%
5 506641
 
5.7%
3 406625
 
4.6%
4 319061
 
3.6%
6 271718
 
3.1%
8 261479
 
3.0%
7 254224
 
2.9%
Other values (2) 222281
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8837652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3505145
39.7%
. 1655222
18.7%
1 807187
 
9.1%
2 628069
 
7.1%
5 506641
 
5.7%
3 406625
 
4.6%
4 319061
 
3.6%
6 271718
 
3.1%
8 261479
 
3.0%
7 254224
 
2.9%
Other values (2) 222281
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8837652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3505145
39.7%
. 1655222
18.7%
1 807187
 
9.1%
2 628069
 
7.1%
5 506641
 
5.7%
3 406625
 
4.6%
4 319061
 
3.6%
6 271718
 
3.1%
8 261479
 
3.0%
7 254224
 
2.9%
Other values (2) 222281
 
2.5%
Distinct2856
Distinct (%)0.6%
Missing4018797
Missing (%)89.0%
Memory size34.5 MiB
2025-03-26T16:25:02.668730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.395856742
Min length3

Characters and Unicode

Total characters2689295
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique706 ?
Unique (%)0.1%

Sample

1st row450.0
2nd row850.0
3rd row792.0
4th row1680.0
5th row1981.0
ValueCountFrequency (%)
1000.0 11913
 
2.4%
600.0 10801
 
2.2%
500.0 10620
 
2.1%
1500.0 10108
 
2.0%
900.0 9850
 
2.0%
1200.0 9382
 
1.9%
100.0 9068
 
1.8%
400.0 8960
 
1.8%
300.0 8476
 
1.7%
2000.0 8255
 
1.7%
Other values (2842) 400967
80.5%
2025-03-26T16:25:02.870587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1146917
42.6%
. 498400
18.5%
1 224485
 
8.3%
2 185868
 
6.9%
5 156415
 
5.8%
3 121744
 
4.5%
4 85344
 
3.2%
6 75401
 
2.8%
8 70405
 
2.6%
7 65945
 
2.5%
Other values (2) 58371
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2689295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1146917
42.6%
. 498400
18.5%
1 224485
 
8.3%
2 185868
 
6.9%
5 156415
 
5.8%
3 121744
 
4.5%
4 85344
 
3.2%
6 75401
 
2.8%
8 70405
 
2.6%
7 65945
 
2.5%
Other values (2) 58371
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2689295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1146917
42.6%
. 498400
18.5%
1 224485
 
8.3%
2 185868
 
6.9%
5 156415
 
5.8%
3 121744
 
4.5%
4 85344
 
3.2%
6 75401
 
2.8%
8 70405
 
2.6%
7 65945
 
2.5%
Other values (2) 58371
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2689295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1146917
42.6%
. 498400
18.5%
1 224485
 
8.3%
2 185868
 
6.9%
5 156415
 
5.8%
3 121744
 
4.5%
4 85344
 
3.2%
6 75401
 
2.8%
8 70405
 
2.6%
7 65945
 
2.5%
Other values (2) 58371
 
2.2%

minimumDepthInMeters
Text

Missing 

Distinct172
Distinct (%)0.4%
Missing4477155
Missing (%)99.1%
Memory size34.5 MiB
2025-03-26T16:25:02.922324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.485964737
Min length3

Characters and Unicode

Total characters139585
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)0.2%

Sample

1st row3.0
2nd row21.0
3rd row3.0
4th row9.0
5th row3.0
ValueCountFrequency (%)
3.0 7623
19.0%
9.0 6774
16.9%
15.0 6026
15.0%
21.0 4195
10.5%
0.0 2655
 
6.6%
37.0 2183
 
5.5%
27.0 1877
 
4.7%
2.0 1319
 
3.3%
12.0 1049
 
2.6%
1.0 878
 
2.2%
Other values (161) 5463
13.6%
2025-03-26T16:25:03.013649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43511
31.2%
. 40042
28.7%
1 14304
 
10.2%
3 10512
 
7.5%
2 9009
 
6.5%
5 7324
 
5.2%
9 7128
 
5.1%
7 4682
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43511
31.2%
. 40042
28.7%
1 14304
 
10.2%
3 10512
 
7.5%
2 9009
 
6.5%
5 7324
 
5.2%
9 7128
 
5.1%
7 4682
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43511
31.2%
. 40042
28.7%
1 14304
 
10.2%
3 10512
 
7.5%
2 9009
 
6.5%
5 7324
 
5.2%
9 7128
 
5.1%
7 4682
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43511
31.2%
. 40042
28.7%
1 14304
 
10.2%
3 10512
 
7.5%
2 9009
 
6.5%
5 7324
 
5.2%
9 7128
 
5.1%
7 4682
 
3.4%
6 1307
 
0.9%
4 929
 
0.7%
Other values (2) 837
 
0.6%

maximumDepthInMeters
Text

Missing 

Distinct80
Distinct (%)0.2%
Missing4480489
Missing (%)99.2%
Memory size34.5 MiB
2025-03-26T16:25:03.054600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.677808652
Min length3

Characters and Unicode

Total characters135005
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.1%

Sample

1st row3.0
2nd row27.0
3rd row3.0
4th row15.0
5th row9.0
ValueCountFrequency (%)
9.0 5796
15.8%
15.0 5744
15.6%
21.0 5323
14.5%
27.0 4190
11.4%
3.0 3061
8.3%
49.0 1873
 
5.1%
37.0 1755
 
4.8%
14.0 1250
 
3.4%
11.0 1073
 
2.9%
5.0 1051
 
2.9%
Other values (70) 5592
15.2%
2025-03-26T16:25:03.148860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37418
27.7%
. 36708
27.2%
1 16753
12.4%
2 10961
 
8.1%
9 7823
 
5.8%
5 7230
 
5.4%
7 6971
 
5.2%
3 5011
 
3.7%
4 3588
 
2.7%
6 1851
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37418
27.7%
. 36708
27.2%
1 16753
12.4%
2 10961
 
8.1%
9 7823
 
5.8%
5 7230
 
5.4%
7 6971
 
5.2%
3 5011
 
3.7%
4 3588
 
2.7%
6 1851
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37418
27.7%
. 36708
27.2%
1 16753
12.4%
2 10961
 
8.1%
9 7823
 
5.8%
5 7230
 
5.4%
7 6971
 
5.2%
3 5011
 
3.7%
4 3588
 
2.7%
6 1851
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37418
27.7%
. 36708
27.2%
1 16753
12.4%
2 10961
 
8.1%
9 7823
 
5.8%
5 7230
 
5.4%
7 6971
 
5.2%
3 5011
 
3.7%
4 3588
 
2.7%
6 1851
 
1.4%

verbatimDepth
Text

Missing 

Distinct18
Distinct (%)0.1%
Missing4495553
Missing (%)99.5%
Memory size34.5 MiB
2025-03-26T16:25:03.178864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length37
Median length3
Mean length3.033866198
Min length2

Characters and Unicode

Total characters65665
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowca.
2nd rowca.
3rd rowca.
4th rowca.
5th rowca.
ValueCountFrequency (%)
ca 21594
99.4%
intertidal 57
 
0.3%
mlw 15
 
0.1%
infralittoral 12
 
0.1%
below 6
 
< 0.1%
above 5
 
< 0.1%
low 5
 
< 0.1%
tide 5
 
< 0.1%
feet 2
 
< 0.1%
cay 2
 
< 0.1%
Other values (20) 22
 
0.1%
2025-03-26T16:25:03.268438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 21687
33.0%
c 21596
32.9%
. 21431
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 21687
33.0%
c 21596
32.9%
. 21431
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 21687
33.0%
c 21596
32.9%
. 21431
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 21687
33.0%
c 21596
32.9%
. 21431
32.6%
t 151
 
0.2%
l 115
 
0.2%
i 97
 
0.1%
e 90
 
0.1%
r 85
 
0.1%
81
 
0.1%
n 70
 
0.1%
Other values (24) 262
 
0.4%

decimalLatitude
Text

Missing 

Distinct65357
Distinct (%)9.7%
Missing3846770
Missing (%)85.2%
Memory size34.5 MiB
2025-03-26T16:25:03.408138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.797360488
Min length3

Characters and Unicode

Total characters3886707
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29345 ?
Unique (%)4.4%

Sample

1st row26.2786
2nd row-35.57
3rd row18.6519
4th row-36.68
5th row5.86667
ValueCountFrequency (%)
38.895 3806
 
0.6%
38.9694 3781
 
0.6%
3.61 1737
 
0.3%
0.83 1705
 
0.3%
9.405 1697
 
0.3%
5.16667 1640
 
0.2%
0.35 1588
 
0.2%
38.8664 1571
 
0.2%
5.2 1489
 
0.2%
12.83 1407
 
0.2%
Other values (59827) 650006
97.0%
2025-03-26T16:25:03.621492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 670427
17.2%
3 456840
11.8%
1 360481
9.3%
8 332480
8.6%
2 330107
8.5%
5 313257
8.1%
6 300583
7.7%
7 272090
7.0%
4 239387
 
6.2%
9 228925
 
5.9%
Other values (2) 382130
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3886707
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 670427
17.2%
3 456840
11.8%
1 360481
9.3%
8 332480
8.6%
2 330107
8.5%
5 313257
8.1%
6 300583
7.7%
7 272090
7.0%
4 239387
 
6.2%
9 228925
 
5.9%
Other values (2) 382130
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3886707
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 670427
17.2%
3 456840
11.8%
1 360481
9.3%
8 332480
8.6%
2 330107
8.5%
5 313257
8.1%
6 300583
7.7%
7 272090
7.0%
4 239387
 
6.2%
9 228925
 
5.9%
Other values (2) 382130
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3886707
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 670427
17.2%
3 456840
11.8%
1 360481
9.3%
8 332480
8.6%
2 330107
8.5%
5 313257
8.1%
6 300583
7.7%
7 272090
7.0%
4 239387
 
6.2%
9 228925
 
5.9%
Other values (2) 382130
9.8%

decimalLongitude
Text

Missing 

Distinct67051
Distinct (%)10.0%
Missing3846771
Missing (%)85.2%
Memory size34.5 MiB
2025-03-26T16:25:03.779499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.78659688
Min length3

Characters and Unicode

Total characters4549911
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27807 ?
Unique (%)4.1%

Sample

1st row-83.7803
2nd row137.32
3rd row-71.5572
4th row-72.97
5th row-60.5667
ValueCountFrequency (%)
77.0367 3742
 
0.6%
77.1767 3713
 
0.6%
59.4833 2410
 
0.4%
53.2 1746
 
0.3%
79.8635 1623
 
0.2%
52.33 1592
 
0.2%
77.7064 1461
 
0.2%
59.48 1420
 
0.2%
70.95 1409
 
0.2%
88.08 1386
 
0.2%
Other values (62116) 649924
96.9%
2025-03-26T16:25:04.002435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 670426
14.7%
- 569293
12.5%
7 495093
10.9%
1 399552
8.8%
6 383401
8.4%
5 381315
8.4%
8 322784
7.1%
3 322315
7.1%
9 271359
6.0%
2 262893
 
5.8%
Other values (2) 471480
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4549911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 670426
14.7%
- 569293
12.5%
7 495093
10.9%
1 399552
8.8%
6 383401
8.4%
5 381315
8.4%
8 322784
7.1%
3 322315
7.1%
9 271359
6.0%
2 262893
 
5.8%
Other values (2) 471480
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4549911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 670426
14.7%
- 569293
12.5%
7 495093
10.9%
1 399552
8.8%
6 383401
8.4%
5 381315
8.4%
8 322784
7.1%
3 322315
7.1%
9 271359
6.0%
2 262893
 
5.8%
Other values (2) 471480
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4549911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 670426
14.7%
- 569293
12.5%
7 495093
10.9%
1 399552
8.8%
6 383401
8.4%
5 381315
8.4%
8 322784
7.1%
3 322315
7.1%
9 271359
6.0%
2 262893
 
5.8%
Other values (2) 471480
10.4%

geodeticDatum
Text

Missing 

Distinct9
Distinct (%)< 0.1%
Missing4487390
Missing (%)99.3%
Memory size34.5 MiB
2025-03-26T16:25:04.049943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length14.73090214
Min length5

Characters and Unicode

Total characters439084
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWGS84
2nd rowWGS 84 (EPSG:4326)
3rd rowWGS84
4th rowWGS 84 (EPSG:4326)
5th rowWGS 84 (EPSG:4326)
ValueCountFrequency (%)
wgs 20857
28.3%
84 20857
28.3%
epsg:4326 20846
28.2%
wgs84 6662
 
9.0%
not 1681
 
2.3%
recorded 1681
 
2.3%
nad83 385
 
0.5%
epsg:4269 385
 
0.5%
nad27 212
 
0.3%
epsg:4267 212
 
0.3%
Other values (4) 20
 
< 0.1%
2025-03-26T16:25:04.134322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 48980
11.2%
G 48980
11.2%
S 48971
11.2%
43991
10.0%
8 27914
 
6.4%
W 27519
 
6.3%
2 21664
 
4.9%
: 21452
 
4.9%
) 21452
 
4.9%
P 21452
 
4.9%
Other values (17) 106709
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 439084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 48980
11.2%
G 48980
11.2%
S 48971
11.2%
43991
10.0%
8 27914
 
6.4%
W 27519
 
6.3%
2 21664
 
4.9%
: 21452
 
4.9%
) 21452
 
4.9%
P 21452
 
4.9%
Other values (17) 106709
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 439084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 48980
11.2%
G 48980
11.2%
S 48971
11.2%
43991
10.0%
8 27914
 
6.4%
W 27519
 
6.3%
2 21664
 
4.9%
: 21452
 
4.9%
) 21452
 
4.9%
P 21452
 
4.9%
Other values (17) 106709
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 439084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 48980
11.2%
G 48980
11.2%
S 48971
11.2%
43991
10.0%
8 27914
 
6.4%
W 27519
 
6.3%
2 21664
 
4.9%
: 21452
 
4.9%
) 21452
 
4.9%
P 21452
 
4.9%
Other values (17) 106709
24.3%
Distinct21
Distinct (%)0.3%
Missing4510725
Missing (%)99.9%
Memory size34.5 MiB
2025-03-26T16:25:04.168322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.866501854
Min length1

Characters and Unicode

Total characters25024
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16000
2nd row1500
3rd row250
4th row500
5th row1500
ValueCountFrequency (%)
16000 1334
20.6%
1000 1306
20.2%
500 1065
16.5%
3000 624
9.6%
250 622
9.6%
750 305
 
4.7%
5000 282
 
4.4%
1500 268
 
4.1%
2000 202
 
3.1%
3500 148
 
2.3%
Other values (11) 316
 
4.9%
2025-03-26T16:25:04.264829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15798
63.1%
1 3055
 
12.2%
5 2772
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15798
63.1%
1 3055
 
12.2%
5 2772
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15798
63.1%
1 3055
 
12.2%
5 2772
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15798
63.1%
1 3055
 
12.2%
5 2772
 
11.1%
6 1370
 
5.5%
2 879
 
3.5%
3 785
 
3.1%
7 305
 
1.2%
8 50
 
0.2%
4 10
 
< 0.1%

verbatimLatitude
Text

Missing 

Distinct5648
Distinct (%)14.9%
Missing4479199
Missing (%)99.2%
Memory size34.5 MiB
2025-03-26T16:25:04.392451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length66
Median length28
Mean length8.208484657
Min length1

Characters and Unicode

Total characters311906
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2970 ?
Unique (%)7.8%

Sample

1st row26 16'43"N
2nd row24 58.74' N
3rd row55 56'N
4th row24 47'31"N
5th row19.75856
ValueCountFrequency (%)
n 20752
23.6%
0 7933
 
9.0%
26 2622
 
3.0%
24 2620
 
3.0%
18 2374
 
2.7%
16 2095
 
2.4%
9 1686
 
1.9%
25 1667
 
1.9%
s 1529
 
1.7%
2.2228 1276
 
1.5%
Other values (4208) 43277
49.3%
2025-03-26T16:25:04.587193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49833
16.0%
N 29869
9.6%
2 27470
8.8%
3 24700
 
7.9%
1 23164
 
7.4%
0 22689
 
7.3%
4 21116
 
6.8%
5 17045
 
5.5%
6 15724
 
5.0%
' 15545
 
5.0%
Other values (44) 64751
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 311906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
49833
16.0%
N 29869
9.6%
2 27470
8.8%
3 24700
 
7.9%
1 23164
 
7.4%
0 22689
 
7.3%
4 21116
 
6.8%
5 17045
 
5.5%
6 15724
 
5.0%
' 15545
 
5.0%
Other values (44) 64751
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 311906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
49833
16.0%
N 29869
9.6%
2 27470
8.8%
3 24700
 
7.9%
1 23164
 
7.4%
0 22689
 
7.3%
4 21116
 
6.8%
5 17045
 
5.5%
6 15724
 
5.0%
' 15545
 
5.0%
Other values (44) 64751
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 311906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
49833
16.0%
N 29869
9.6%
2 27470
8.8%
3 24700
 
7.9%
1 23164
 
7.4%
0 22689
 
7.3%
4 21116
 
6.8%
5 17045
 
5.5%
6 15724
 
5.0%
' 15545
 
5.0%
Other values (44) 64751
20.8%

verbatimLongitude
Text

Missing 

Distinct5595
Distinct (%)14.7%
Missing4479213
Missing (%)99.2%
Memory size34.5 MiB
2025-03-26T16:25:04.719015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length68
Median length29
Mean length8.567107203
Min length1

Characters and Unicode

Total characters325413
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2938 ?
Unique (%)7.7%

Sample

1st row83 46'49"W
2nd row76 12.75' W
3rd row11 55'E
4th row83 41'11"W
5th row-97.63925
ValueCountFrequency (%)
w 11525
 
13.1%
e 10550
 
12.0%
0 7784
 
8.8%
82 2502
 
2.8%
88 1825
 
2.1%
79 1618
 
1.8%
83 1561
 
1.8%
51 1335
 
1.5%
9.91722 1276
 
1.4%
48.5 1155
 
1.3%
Other values (4272) 46894
53.3%
2025-03-26T16:25:04.918975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50041
15.4%
0 24934
 
7.7%
1 24700
 
7.6%
7 23710
 
7.3%
W 20636
 
6.3%
2 19469
 
6.0%
4 19367
 
6.0%
5 19040
 
5.9%
8 18809
 
5.8%
3 16068
 
4.9%
Other values (40) 88639
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
50041
15.4%
0 24934
 
7.7%
1 24700
 
7.6%
7 23710
 
7.3%
W 20636
 
6.3%
2 19469
 
6.0%
4 19367
 
6.0%
5 19040
 
5.9%
8 18809
 
5.8%
3 16068
 
4.9%
Other values (40) 88639
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
50041
15.4%
0 24934
 
7.7%
1 24700
 
7.6%
7 23710
 
7.3%
W 20636
 
6.3%
2 19469
 
6.0%
4 19367
 
6.0%
5 19040
 
5.9%
8 18809
 
5.8%
3 16068
 
4.9%
Other values (40) 88639
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
50041
15.4%
0 24934
 
7.7%
1 24700
 
7.6%
7 23710
 
7.3%
W 20636
 
6.3%
2 19469
 
6.0%
4 19367
 
6.0%
5 19040
 
5.9%
8 18809
 
5.8%
3 16068
 
4.9%
Other values (40) 88639
27.2%
Distinct4
Distinct (%)< 0.1%
Missing4480154
Missing (%)99.2%
Memory size34.5 MiB
2025-03-26T16:25:04.955011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.9807251
Min length4

Characters and Unicode

Total characters851275
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 37015
33.3%
minutes 36990
33.3%
seconds 36990
33.3%
decimal 25
 
< 0.1%
quad 22
 
< 0.1%
unknown 6
 
< 0.1%
2025-03-26T16:25:05.045171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 185050
21.7%
s 110995
13.0%
74005
 
8.7%
n 73998
 
8.7%
D 37037
 
4.4%
g 37015
 
4.3%
r 37015
 
4.3%
d 37015
 
4.3%
i 37015
 
4.3%
c 37015
 
4.3%
Other values (13) 185115
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 851275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 185050
21.7%
s 110995
13.0%
74005
 
8.7%
n 73998
 
8.7%
D 37037
 
4.4%
g 37015
 
4.3%
r 37015
 
4.3%
d 37015
 
4.3%
i 37015
 
4.3%
c 37015
 
4.3%
Other values (13) 185115
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 851275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 185050
21.7%
s 110995
13.0%
74005
 
8.7%
n 73998
 
8.7%
D 37037
 
4.4%
g 37015
 
4.3%
r 37015
 
4.3%
d 37015
 
4.3%
i 37015
 
4.3%
c 37015
 
4.3%
Other values (13) 185115
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 851275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 185050
21.7%
s 110995
13.0%
74005
 
8.7%
n 73998
 
8.7%
D 37037
 
4.4%
g 37015
 
4.3%
r 37015
 
4.3%
d 37015
 
4.3%
i 37015
 
4.3%
c 37015
 
4.3%
Other values (13) 185115
21.7%

georeferenceProtocol
Text

Missing 

Distinct20
Distinct (%)< 0.1%
Missing4390038
Missing (%)97.2%
Memory size34.5 MiB
2025-03-26T16:25:05.076175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length8.340282638
Min length3

Characters and Unicode

Total characters1060542
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGazetteer
2nd rowGazetteer
3rd rowGazetteer
4th rowGazetteer
5th rowLabel
ValueCountFrequency (%)
gazetteer 49186
29.7%
gps 23401
14.1%
gis 20976
12.7%
arcview 20976
12.7%
label 17066
 
10.3%
google 15527
 
9.4%
maps 12430
 
7.5%
earth 3097
 
1.9%
source 1688
 
1.0%
g-1 398
 
0.2%
Other values (11) 706
 
0.4%
2025-03-26T16:25:05.163754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 203104
19.2%
G 109445
 
10.3%
t 101529
 
9.6%
a 82105
 
7.7%
r 75005
 
7.1%
z 49186
 
4.6%
S 46007
 
4.3%
38292
 
3.6%
o 32960
 
3.1%
l 32704
 
3.1%
Other values (30) 290205
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1060542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 203104
19.2%
G 109445
 
10.3%
t 101529
 
9.6%
a 82105
 
7.7%
r 75005
 
7.1%
z 49186
 
4.6%
S 46007
 
4.3%
38292
 
3.6%
o 32960
 
3.1%
l 32704
 
3.1%
Other values (30) 290205
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1060542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 203104
19.2%
G 109445
 
10.3%
t 101529
 
9.6%
a 82105
 
7.7%
r 75005
 
7.1%
z 49186
 
4.6%
S 46007
 
4.3%
38292
 
3.6%
o 32960
 
3.1%
l 32704
 
3.1%
Other values (30) 290205
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1060542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 203104
19.2%
G 109445
 
10.3%
t 101529
 
9.6%
a 82105
 
7.7%
r 75005
 
7.1%
z 49186
 
4.6%
S 46007
 
4.3%
38292
 
3.6%
o 32960
 
3.1%
l 32704
 
3.1%
Other values (30) 290205
27.4%

georeferenceRemarks
Text

Missing 

Distinct78
Distinct (%)15.3%
Missing4516686
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-26T16:25:05.229113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length67
Median length57
Mean length19.70254403
Min length1

Characters and Unicode

Total characters10068
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)5.7%

Sample

1st row+-1000m
2nd rowstop 1 - beginning of bike path, along GW pkwy
3rd rowca.; ca.
4th rowstop 1-ditch; stop 2- polkweed; stop 3; stop 4
5th rowLong. 4 8 W - 4 15 W
ValueCountFrequency (%)
stop 226
 
10.5%
4 144
 
6.7%
119
 
5.5%
w 106
 
4.9%
ca 90
 
4.2%
1 86
 
4.0%
seconds 56
 
2.6%
long 53
 
2.5%
15 53
 
2.5%
8 53
 
2.5%
Other values (116) 1167
54.2%
2025-03-26T16:25:05.368977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1642
16.3%
o 738
 
7.3%
t 614
 
6.1%
e 593
 
5.9%
a 549
 
5.5%
n 536
 
5.3%
i 494
 
4.9%
s 437
 
4.3%
p 406
 
4.0%
l 382
 
3.8%
Other values (54) 3677
36.5%
Distinct17
Distinct (%)0.2%
Missing4506191
Missing (%)99.8%
Memory size34.5 MiB
2025-03-26T16:25:05.404976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length3
Mean length4.356896238
Min length2

Characters and Unicode

Total characters47952
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcf.
2nd rowcf.
3rd rowcf.
4th rowvel aff.
5th rowvel aff.
ValueCountFrequency (%)
cf 5897
51.5%
aff 2850
24.9%
uncertain 1610
 
14.1%
s.l 543
 
4.7%
vel 347
 
3.0%
near 76
 
0.7%
sp 64
 
0.6%
nov 42
 
0.4%
s.s 27
 
0.2%
2025-03-26T16:25:05.493294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 11597
24.2%
. 9929
20.7%
c 7507
15.7%
a 4536
 
9.5%
n 3338
 
7.0%
e 2033
 
4.2%
r 1686
 
3.5%
i 1610
 
3.4%
t 1610
 
3.4%
u 1600
 
3.3%
Other values (7) 2506
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 11597
24.2%
. 9929
20.7%
c 7507
15.7%
a 4536
 
9.5%
n 3338
 
7.0%
e 2033
 
4.2%
r 1686
 
3.5%
i 1610
 
3.4%
t 1610
 
3.4%
u 1600
 
3.3%
Other values (7) 2506
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 11597
24.2%
. 9929
20.7%
c 7507
15.7%
a 4536
 
9.5%
n 3338
 
7.0%
e 2033
 
4.2%
r 1686
 
3.5%
i 1610
 
3.4%
t 1610
 
3.4%
u 1600
 
3.3%
Other values (7) 2506
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 11597
24.2%
. 9929
20.7%
c 7507
15.7%
a 4536
 
9.5%
n 3338
 
7.0%
e 2033
 
4.2%
r 1686
 
3.5%
i 1610
 
3.4%
t 1610
 
3.4%
u 1600
 
3.3%
Other values (7) 2506
 
5.2%

typeStatus
Text

Missing 

Distinct192
Distinct (%)0.2%
Missing4400798
Missing (%)97.4%
Memory size34.5 MiB
2025-03-26T16:25:05.524796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length7
Mean length8.824233885
Min length4

Characters and Unicode

Total characters1027132
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.1%

Sample

1st rowIsotype
2nd rowIsotype
3rd rowHolotype
4th rowType Collection
5th rowType Collection
ValueCountFrequency (%)
isotype 61621
45.1%
holotype 19982
 
14.6%
type 16853
 
12.3%
collection 9782
 
7.2%
isosyntype 7055
 
5.2%
syntype 6303
 
4.6%
fragment 5520
 
4.0%
isolectotype 2871
 
2.1%
possible 2535
 
1.9%
lectotype 1378
 
1.0%
Other values (16) 2656
 
1.9%
2025-03-26T16:25:05.624681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 141437
13.8%
o 139747
13.6%
y 131154
12.8%
t 121792
11.9%
p 117874
11.5%
s 84743
8.3%
I 72017
7.0%
l 46340
 
4.5%
n 29160
 
2.8%
20157
 
2.0%
Other values (26) 122711
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1027132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141437
13.8%
o 139747
13.6%
y 131154
12.8%
t 121792
11.9%
p 117874
11.5%
s 84743
8.3%
I 72017
7.0%
l 46340
 
4.5%
n 29160
 
2.8%
20157
 
2.0%
Other values (26) 122711
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1027132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141437
13.8%
o 139747
13.6%
y 131154
12.8%
t 121792
11.9%
p 117874
11.5%
s 84743
8.3%
I 72017
7.0%
l 46340
 
4.5%
n 29160
 
2.8%
20157
 
2.0%
Other values (26) 122711
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1027132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141437
13.8%
o 139747
13.6%
y 131154
12.8%
t 121792
11.9%
p 117874
11.5%
s 84743
8.3%
I 72017
7.0%
l 46340
 
4.5%
n 29160
 
2.8%
20157
 
2.0%
Other values (26) 122711
11.9%

identifiedBy
Text

Missing 

Distinct8135
Distinct (%)1.5%
Missing3959451
Missing (%)87.7%
Memory size34.5 MiB
2025-03-26T16:25:05.749019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length131
Median length109
Mean length37.7214162
Min length2

Characters and Unicode

Total characters21038969
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2642 ?
Unique (%)0.5%

Sample

1st rowBlair, S. M.
2nd rowAcevedo-Rodríguez, P., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
3rd rowAcevedo-Rodríguez, P., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
4th rowWagner, W. L., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
5th rowWagner, W. L., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
ValueCountFrequency (%)
united 136586
 
4.2%
states 136540
 
4.2%
of 126323
 
3.8%
123632
 
3.8%
national 120708
 
3.7%
museum 119610
 
3.6%
smithsonian 118993
 
3.6%
natural 118804
 
3.6%
history 118697
 
3.6%
institution 118681
 
3.6%
Other values (6318) 2047162
62.3%
2025-03-26T16:25:05.959754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2727990
 
13.0%
t 1182022
 
5.6%
a 1144804
 
5.4%
o 1117602
 
5.3%
i 1047317
 
5.0%
n 1030386
 
4.9%
, 908181
 
4.3%
. 858560
 
4.1%
r 854241
 
4.1%
e 836345
 
4.0%
Other values (88) 9331521
44.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21038969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2727990
 
13.0%
t 1182022
 
5.6%
a 1144804
 
5.4%
o 1117602
 
5.3%
i 1047317
 
5.0%
n 1030386
 
4.9%
, 908181
 
4.3%
. 858560
 
4.1%
r 854241
 
4.1%
e 836345
 
4.0%
Other values (88) 9331521
44.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21038969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2727990
 
13.0%
t 1182022
 
5.6%
a 1144804
 
5.4%
o 1117602
 
5.3%
i 1047317
 
5.0%
n 1030386
 
4.9%
, 908181
 
4.3%
. 858560
 
4.1%
r 854241
 
4.1%
e 836345
 
4.0%
Other values (88) 9331521
44.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21038969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2727990
 
13.0%
t 1182022
 
5.6%
a 1144804
 
5.4%
o 1117602
 
5.3%
i 1047317
 
5.0%
n 1030386
 
4.9%
, 908181
 
4.3%
. 858560
 
4.1%
r 854241
 
4.1%
e 836345
 
4.0%
Other values (88) 9331521
44.4%
Distinct330730
Distinct (%)7.3%
Missing13710
Missing (%)0.3%
Memory size34.5 MiB
2025-03-26T16:25:06.132740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length136
Median length98
Mean length19.79001116
Min length5

Characters and Unicode

Total characters89124058
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121033 ?
Unique (%)2.7%

Sample

1st rowLithothamnion calcareum
2nd rowAmicia glandulosa
3rd rowTripogandra glandulosa
4th rowConnarus steyermarkii
5th rowTrichoneura grandiglumis
ValueCountFrequency (%)
sp 270341
 
2.8%
var 210247
 
2.2%
subsp 106202
 
1.1%
carex 58149
 
0.6%
indet 41318
 
0.4%
poa 30200
 
0.3%
cyperus 27853
 
0.3%
cladonia 27032
 
0.3%
paspalum 26150
 
0.3%
solanum 24861
 
0.3%
Other values (98725) 8924721
91.6%
2025-03-26T16:25:06.375183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10715771
 
12.0%
i 8474253
 
9.5%
e 5758819
 
6.5%
s 5578790
 
6.3%
r 5528054
 
6.2%
5243587
 
5.9%
o 5209523
 
5.8%
l 4894918
 
5.5%
n 4700089
 
5.3%
u 4560859
 
5.1%
Other values (87) 28459395
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89124058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10715771
 
12.0%
i 8474253
 
9.5%
e 5758819
 
6.5%
s 5578790
 
6.3%
r 5528054
 
6.2%
5243587
 
5.9%
o 5209523
 
5.8%
l 4894918
 
5.5%
n 4700089
 
5.3%
u 4560859
 
5.1%
Other values (87) 28459395
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89124058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10715771
 
12.0%
i 8474253
 
9.5%
e 5758819
 
6.5%
s 5578790
 
6.3%
r 5528054
 
6.2%
5243587
 
5.9%
o 5209523
 
5.8%
l 4894918
 
5.5%
n 4700089
 
5.3%
u 4560859
 
5.1%
Other values (87) 28459395
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89124058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10715771
 
12.0%
i 8474253
 
9.5%
e 5758819
 
6.5%
s 5578790
 
6.3%
r 5528054
 
6.2%
5243587
 
5.9%
o 5209523
 
5.8%
l 4894918
 
5.5%
n 4700089
 
5.3%
u 4560859
 
5.1%
Other values (87) 28459395
31.9%
Distinct2219
Distinct (%)< 0.1%
Missing13868
Missing (%)0.3%
Memory size34.5 MiB
2025-03-26T16:25:06.427002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length135
Median length89
Mean length55.77143331
Min length6

Characters and Unicode

Total characters251157113
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)< 0.1%

Sample

1st rowPlantae, Rhodophyta, Corallinales, Lithothamniaceae
2nd rowPlantae, Dicotyledonae, Fabales, Fabaceae, Papilionoideae
3rd rowPlantae, Monocotyledonae, Commelinales, Commelinaceae
4th rowPlantae, Dicotyledonae, Oxalidales, Connaraceae
5th rowPlantae, Monocotyledonae, Poales, Poaceae, Chloridoideae
ValueCountFrequency (%)
plantae 4145164
 
19.6%
dicotyledonae 2584430
 
12.2%
monocotyledonae 909327
 
4.3%
poales 702399
 
3.3%
poaceae 502569
 
2.4%
asterales 380380
 
1.8%
asteraceae 358421
 
1.7%
asteroideae 283092
 
1.3%
pteridophyte 276717
 
1.3%
lamiales 266472
 
1.3%
Other values (2230) 10772863
50.9%
2025-03-26T16:25:06.542475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35384310
14.1%
e 35173155
14.0%
o 18863554
 
7.5%
16678505
 
6.6%
, 16568863
 
6.6%
l 16278520
 
6.5%
n 12775002
 
5.1%
t 12546678
 
5.0%
i 12461728
 
5.0%
c 11343457
 
4.5%
Other values (50) 63083341
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 251157113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35384310
14.1%
e 35173155
14.0%
o 18863554
 
7.5%
16678505
 
6.6%
, 16568863
 
6.6%
l 16278520
 
6.5%
n 12775002
 
5.1%
t 12546678
 
5.0%
i 12461728
 
5.0%
c 11343457
 
4.5%
Other values (50) 63083341
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 251157113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35384310
14.1%
e 35173155
14.0%
o 18863554
 
7.5%
16678505
 
6.6%
, 16568863
 
6.6%
l 16278520
 
6.5%
n 12775002
 
5.1%
t 12546678
 
5.0%
i 12461728
 
5.0%
c 11343457
 
4.5%
Other values (50) 63083341
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 251157113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35384310
14.1%
e 35173155
14.0%
o 18863554
 
7.5%
16678505
 
6.6%
, 16568863
 
6.6%
l 16278520
 
6.5%
n 12775002
 
5.1%
t 12546678
 
5.0%
i 12461728
 
5.0%
c 11343457
 
4.5%
Other values (50) 63083341
25.1%
Distinct12
Distinct (%)< 0.1%
Missing15574
Missing (%)0.3%
Memory size34.5 MiB
2025-03-26T16:25:06.573477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length7
Mean length6.96301956
Min length5

Characters and Unicode

Total characters31344889
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPlantae
2nd rowPlantae
3rd rowPlantae
4th rowPlantae
5th rowPlantae
ValueCountFrequency (%)
plantae 4145125
92.1%
fungi 223345
 
5.0%
eubacteria 52572
 
1.2%
chromista 41853
 
0.9%
protista 38699
 
0.9%
protozoa 24
 
< 0.1%
incertae 3
 
< 0.1%
sedis 3
 
< 0.1%
prokaryota 2
 
< 0.1%
kingdom 2
 
< 0.1%
Other values (3) 3
 
< 0.1%
2025-03-26T16:25:06.658501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8475982
27.0%
n 4368477
13.9%
t 4316979
13.8%
e 4197708
13.4%
P 4183847
13.3%
l 4145125
13.2%
i 356478
 
1.1%
u 275919
 
0.9%
g 223348
 
0.7%
F 223343
 
0.7%
Other values (18) 577683
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31344889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8475982
27.0%
n 4368477
13.9%
t 4316979
13.8%
e 4197708
13.4%
P 4183847
13.3%
l 4145125
13.2%
i 356478
 
1.1%
u 275919
 
0.9%
g 223348
 
0.7%
F 223343
 
0.7%
Other values (18) 577683
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31344889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8475982
27.0%
n 4368477
13.9%
t 4316979
13.8%
e 4197708
13.4%
P 4183847
13.3%
l 4145125
13.2%
i 356478
 
1.1%
u 275919
 
0.9%
g 223348
 
0.7%
F 223343
 
0.7%
Other values (18) 577683
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31344889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8475982
27.0%
n 4368477
13.9%
t 4316979
13.8%
e 4197708
13.4%
P 4183847
13.3%
l 4145125
13.2%
i 356478
 
1.1%
u 275919
 
0.9%
g 223348
 
0.7%
F 223343
 
0.7%
Other values (18) 577683
 
1.8%

phylum
Text

Missing 

Distinct37
Distinct (%)< 0.1%
Missing3796598
Missing (%)84.0%
Memory size34.5 MiB
2025-03-26T16:25:06.693501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length10
Mean length10.45429705
Min length6

Characters and Unicode

Total characters7533356
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowRhodophyta
2nd rowBryophyta
3rd rowAscomycota
4th rowRhodophyta
5th rowBryophyta
ValueCountFrequency (%)
ascomycota 220456
30.6%
bryophyta 148426
20.6%
rhodophyta 121661
16.9%
cyanobacteria 52567
 
7.3%
chlorophyta 44647
 
6.2%
bacillariophyta 33233
 
4.6%
ochrophyta 29642
 
4.1%
marchantiophyta 26568
 
3.7%
pinophyta 21237
 
2.9%
miozoa 4849
 
0.7%
Other values (29) 18260
 
2.5%
2025-03-26T16:25:06.781011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1114990
14.8%
a 950749
12.6%
y 866630
11.5%
t 747620
9.9%
h 664949
8.8%
c 587082
7.8%
p 438265
 
5.8%
r 348810
 
4.6%
s 224330
 
3.0%
m 222377
 
3.0%
Other values (26) 1367554
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7533356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1114990
14.8%
a 950749
12.6%
y 866630
11.5%
t 747620
9.9%
h 664949
8.8%
c 587082
7.8%
p 438265
 
5.8%
r 348810
 
4.6%
s 224330
 
3.0%
m 222377
 
3.0%
Other values (26) 1367554
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7533356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1114990
14.8%
a 950749
12.6%
y 866630
11.5%
t 747620
9.9%
h 664949
8.8%
c 587082
7.8%
p 438265
 
5.8%
r 348810
 
4.6%
s 224330
 
3.0%
m 222377
 
3.0%
Other values (26) 1367554
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7533356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1114990
14.8%
a 950749
12.6%
y 866630
11.5%
t 747620
9.9%
h 664949
8.8%
c 587082
7.8%
p 438265
 
5.8%
r 348810
 
4.6%
s 224330
 
3.0%
m 222377
 
3.0%
Other values (26) 1367554
18.2%

class
Text

Missing 

Distinct88
Distinct (%)< 0.1%
Missing166473
Missing (%)3.7%
Memory size34.5 MiB
2025-03-26T16:25:06.814014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length13
Mean length13.51309805
Min length6

Characters and Unicode

Total characters58791760
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowDicotyledonae
2nd rowMonocotyledonae
3rd rowDicotyledonae
4th rowMonocotyledonae
5th rowDicotyledonae
ValueCountFrequency (%)
dicotyledonae 2584429
58.2%
monocotyledonae 909327
 
20.5%
pteridophyte 276717
 
6.2%
lecanoromycetes 203063
 
4.6%
bryopsida 127712
 
2.9%
florideophyceae 89491
 
2.0%
basal 85214
 
1.9%
ulvophyceae 35539
 
0.8%
jungermanniopsida 25881
 
0.6%
pinopsida 23214
 
0.5%
Other values (80) 76557
 
1.7%
2025-03-26T16:25:06.902310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 9983595
17.0%
e 8654387
14.7%
n 4735007
8.1%
t 4295399
7.3%
y 4290570
7.3%
a 4289959
7.3%
c 4091430
7.0%
d 4071779
6.9%
l 3714656
 
6.3%
i 3239272
 
5.5%
Other values (37) 7425706
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58791760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 9983595
17.0%
e 8654387
14.7%
n 4735007
8.1%
t 4295399
7.3%
y 4290570
7.3%
a 4289959
7.3%
c 4091430
7.0%
d 4071779
6.9%
l 3714656
 
6.3%
i 3239272
 
5.5%
Other values (37) 7425706
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58791760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 9983595
17.0%
e 8654387
14.7%
n 4735007
8.1%
t 4295399
7.3%
y 4290570
7.3%
a 4289959
7.3%
c 4091430
7.0%
d 4071779
6.9%
l 3714656
 
6.3%
i 3239272
 
5.5%
Other values (37) 7425706
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58791760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 9983595
17.0%
e 8654387
14.7%
n 4735007
8.1%
t 4295399
7.3%
y 4290570
7.3%
a 4289959
7.3%
c 4091430
7.0%
d 4071779
6.9%
l 3714656
 
6.3%
i 3239272
 
5.5%
Other values (37) 7425706
12.6%

order
Text

Missing 

Distinct404
Distinct (%)< 0.1%
Missing53018
Missing (%)1.2%
Memory size34.5 MiB
2025-03-26T16:25:07.035460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length31
Mean length9.299697212
Min length6

Characters and Unicode

Total characters41515513
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st rowCorallinales
2nd rowFabales
3rd rowCommelinales
4th rowOxalidales
5th rowPoales
ValueCountFrequency (%)
poales 702399
 
15.7%
asterales 380379
 
8.5%
lamiales 266472
 
6.0%
fabales 254688
 
5.7%
malpighiales 211690
 
4.7%
polypodiales 193154
 
4.3%
gentianales 180642
 
4.0%
myrtales 158362
 
3.5%
caryophyllales 147716
 
3.3%
ericales 129800
 
2.9%
Other values (398) 1840605
41.2%
2025-03-26T16:25:07.239235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6799347
16.4%
l 5860253
14.1%
e 5563480
13.4%
s 5365895
12.9%
i 2261122
 
5.4%
o 2044419
 
4.9%
r 1727368
 
4.2%
n 1206838
 
2.9%
t 1061013
 
2.6%
P 1016927
 
2.4%
Other values (41) 8608851
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41515513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6799347
16.4%
l 5860253
14.1%
e 5563480
13.4%
s 5365895
12.9%
i 2261122
 
5.4%
o 2044419
 
4.9%
r 1727368
 
4.2%
n 1206838
 
2.9%
t 1061013
 
2.6%
P 1016927
 
2.4%
Other values (41) 8608851
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41515513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6799347
16.4%
l 5860253
14.1%
e 5563480
13.4%
s 5365895
12.9%
i 2261122
 
5.4%
o 2044419
 
4.9%
r 1727368
 
4.2%
n 1206838
 
2.9%
t 1061013
 
2.6%
P 1016927
 
2.4%
Other values (41) 8608851
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41515513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6799347
16.4%
l 5860253
14.1%
e 5563480
13.4%
s 5365895
12.9%
i 2261122
 
5.4%
o 2044419
 
4.9%
r 1727368
 
4.2%
n 1206838
 
2.9%
t 1061013
 
2.6%
P 1016927
 
2.4%
Other values (41) 8608851
20.7%

family
Text

Missing 

Distinct1349
Distinct (%)< 0.1%
Missing49037
Missing (%)1.1%
Memory size34.5 MiB
2025-03-26T16:25:07.379878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length34
Mean length10.7701423
Min length6

Characters and Unicode

Total characters48122719
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)< 0.1%

Sample

1st rowLithothamniaceae
2nd rowFabaceae
3rd rowCommelinaceae
4th rowConnaraceae
5th rowPoaceae
ValueCountFrequency (%)
poaceae 502569
 
11.2%
asteraceae 358421
 
8.0%
fabaceae 238008
 
5.3%
cyperaceae 139835
 
3.1%
rubiaceae 119921
 
2.7%
melastomataceae 73774
 
1.6%
parmeliaceae 66957
 
1.5%
rosaceae 65753
 
1.5%
lamiaceae 62383
 
1.4%
euphorbiaceae 59319
 
1.3%
Other values (1336) 2801175
62.4%
2025-03-26T16:25:07.589617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10982175
22.8%
e 10627736
22.1%
c 5309942
11.0%
i 2197161
 
4.6%
r 2092469
 
4.3%
o 2020936
 
4.2%
l 1543148
 
3.2%
t 1385680
 
2.9%
n 1301153
 
2.7%
s 1002783
 
2.1%
Other values (47) 9659536
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48122719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10982175
22.8%
e 10627736
22.1%
c 5309942
11.0%
i 2197161
 
4.6%
r 2092469
 
4.3%
o 2020936
 
4.2%
l 1543148
 
3.2%
t 1385680
 
2.9%
n 1301153
 
2.7%
s 1002783
 
2.1%
Other values (47) 9659536
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48122719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10982175
22.8%
e 10627736
22.1%
c 5309942
11.0%
i 2197161
 
4.6%
r 2092469
 
4.3%
o 2020936
 
4.2%
l 1543148
 
3.2%
t 1385680
 
2.9%
n 1301153
 
2.7%
s 1002783
 
2.1%
Other values (47) 9659536
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48122719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10982175
22.8%
e 10627736
22.1%
c 5309942
11.0%
i 2197161
 
4.6%
r 2092469
 
4.3%
o 2020936
 
4.2%
l 1543148
 
3.2%
t 1385680
 
2.9%
n 1301153
 
2.7%
s 1002783
 
2.1%
Other values (47) 9659536
20.1%

genus
Text

Distinct19533
Distinct (%)0.4%
Missing13778
Missing (%)0.3%
Memory size34.5 MiB
2025-03-26T16:25:07.723921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length21
Mean length8.779875912
Min length2

Characters and Unicode

Total characters39539460
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2722 ?
Unique (%)0.1%

Sample

1st rowLithothamnion
2nd rowAmicia
3rd rowTripogandra
4th rowConnarus
5th rowTrichoneura
ValueCountFrequency (%)
carex 58149
 
1.3%
indet 34186
 
0.8%
poa 30200
 
0.7%
cyperus 27853
 
0.6%
cladonia 26936
 
0.6%
paspalum 26150
 
0.6%
solanum 24861
 
0.6%
miconia 24584
 
0.5%
eragrostis 23712
 
0.5%
asplenium 20043
 
0.4%
Other values (19524) 4221610
93.4%
2025-03-26T16:25:07.922386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4834684
 
12.2%
i 3639570
 
9.2%
o 2766891
 
7.0%
e 2764940
 
7.0%
r 2583768
 
6.5%
l 2162690
 
5.5%
n 2071418
 
5.2%
s 2058918
 
5.2%
u 1993772
 
5.0%
t 1686449
 
4.3%
Other values (49) 12976360
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39539460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4834684
 
12.2%
i 3639570
 
9.2%
o 2766891
 
7.0%
e 2764940
 
7.0%
r 2583768
 
6.5%
l 2162690
 
5.5%
n 2071418
 
5.2%
s 2058918
 
5.2%
u 1993772
 
5.0%
t 1686449
 
4.3%
Other values (49) 12976360
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39539460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4834684
 
12.2%
i 3639570
 
9.2%
o 2766891
 
7.0%
e 2764940
 
7.0%
r 2583768
 
6.5%
l 2162690
 
5.5%
n 2071418
 
5.2%
s 2058918
 
5.2%
u 1993772
 
5.0%
t 1686449
 
4.3%
Other values (49) 12976360
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39539460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4834684
 
12.2%
i 3639570
 
9.2%
o 2766891
 
7.0%
e 2764940
 
7.0%
r 2583768
 
6.5%
l 2162690
 
5.5%
n 2071418
 
5.2%
s 2058918
 
5.2%
u 1993772
 
5.0%
t 1686449
 
4.3%
Other values (49) 12976360
32.8%

subgenus
Text

Missing 

Distinct14
Distinct (%)15.7%
Missing4517108
Missing (%)> 99.9%
Memory size34.5 MiB
2025-03-26T16:25:07.975456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length12.14606742
Min length6

Characters and Unicode

Total characters1081
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)5.6%

Sample

1st rowChoanopsis
2nd rowLeptostemonum
3rd rowLeptostemonum
4th rowPseudopoa
5th rowLeptostemonum
ValueCountFrequency (%)
leptostemonum 41
45.6%
meniscium 13
 
14.4%
goniophlebiopteris 10
 
11.1%
pseudopoa 6
 
6.7%
choanopsis 5
 
5.6%
penzigia 3
 
3.3%
arenariae 2
 
2.2%
trichochloa 2
 
2.2%
pseudolysimachium 2
 
2.2%
limnochloa 1
 
1.1%
Other values (5) 5
 
5.6%
2025-03-26T16:25:08.063084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 147
13.6%
e 131
12.1%
m 100
9.3%
t 93
8.6%
s 84
7.8%
i 82
7.6%
n 78
7.2%
p 74
6.8%
u 66
 
6.1%
L 42
 
3.9%
Other values (22) 184
17.0%
Distinct75727
Distinct (%)1.7%
Missing19430
Missing (%)0.4%
Memory size34.5 MiB
2025-03-26T16:25:08.202651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length8.783453434
Min length2

Characters and Unicode

Total characters39505927
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20953 ?
Unique (%)0.5%

Sample

1st rowcalcareum
2nd rowglandulosa
3rd rowglandulosa
4th rowsteyermarkii
5th rowgrandiglumis
ValueCountFrequency (%)
sp 270421
 
6.0%
canadensis 11710
 
0.3%
guianensis 11468
 
0.3%
americana 11281
 
0.3%
latifolia 11154
 
0.2%
repens 10155
 
0.2%
parviflora 10011
 
0.2%
occidentalis 9669
 
0.2%
gracilis 9143
 
0.2%
indica 9042
 
0.2%
Other values (75614) 4135311
91.9%
2025-03-26T16:25:08.421719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5239190
13.3%
i 4470475
11.3%
s 3077373
 
7.8%
e 2757607
 
7.0%
r 2533042
 
6.4%
l 2516524
 
6.4%
n 2416008
 
6.1%
u 2269692
 
5.7%
o 2257049
 
5.7%
t 2041933
 
5.2%
Other values (45) 9927034
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39505927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5239190
13.3%
i 4470475
11.3%
s 3077373
 
7.8%
e 2757607
 
7.0%
r 2533042
 
6.4%
l 2516524
 
6.4%
n 2416008
 
6.1%
u 2269692
 
5.7%
o 2257049
 
5.7%
t 2041933
 
5.2%
Other values (45) 9927034
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39505927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5239190
13.3%
i 4470475
11.3%
s 3077373
 
7.8%
e 2757607
 
7.0%
r 2533042
 
6.4%
l 2516524
 
6.4%
n 2416008
 
6.1%
u 2269692
 
5.7%
o 2257049
 
5.7%
t 2041933
 
5.2%
Other values (45) 9927034
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39505927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5239190
13.3%
i 4470475
11.3%
s 3077373
 
7.8%
e 2757607
 
7.0%
r 2533042
 
6.4%
l 2516524
 
6.4%
n 2416008
 
6.1%
u 2269692
 
5.7%
o 2257049
 
5.7%
t 2041933
 
5.2%
Other values (45) 9927034
25.1%

infraspecificEpithet
Text

Missing 

Distinct13514
Distinct (%)4.2%
Missing4197487
Missing (%)92.9%
Memory size34.5 MiB
2025-03-26T16:25:08.535763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length33
Median length29
Mean length9.193243877
Min length1

Characters and Unicode

Total characters2939172
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4777 ?
Unique (%)1.5%

Sample

1st rowoxyphylla
2nd rowsubalpinum
3rd rowpurpurescens
4th rowpubescens
5th rowhirsuta
ValueCountFrequency (%)
acuminatum 4369
 
1.4%
pubescens 1879
 
0.6%
secunda 1646
 
0.5%
dichotomum 1522
 
0.5%
americana 1487
 
0.5%
gracilis 1466
 
0.5%
angustifolia 1339
 
0.4%
typica 1218
 
0.4%
occidentalis 1214
 
0.4%
glauca 1198
 
0.4%
Other values (13465) 302823
94.6%
2025-03-26T16:25:08.711565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 401361
13.7%
i 336290
11.4%
s 216260
 
7.4%
e 204841
 
7.0%
l 195614
 
6.7%
n 184257
 
6.3%
r 181813
 
6.2%
u 176751
 
6.0%
o 167476
 
5.7%
t 150099
 
5.1%
Other values (38) 724410
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2939172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 401361
13.7%
i 336290
11.4%
s 216260
 
7.4%
e 204841
 
7.0%
l 195614
 
6.7%
n 184257
 
6.3%
r 181813
 
6.2%
u 176751
 
6.0%
o 167476
 
5.7%
t 150099
 
5.1%
Other values (38) 724410
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2939172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 401361
13.7%
i 336290
11.4%
s 216260
 
7.4%
e 204841
 
7.0%
l 195614
 
6.7%
n 184257
 
6.3%
r 181813
 
6.2%
u 176751
 
6.0%
o 167476
 
5.7%
t 150099
 
5.1%
Other values (38) 724410
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2939172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 401361
13.7%
i 336290
11.4%
s 216260
 
7.4%
e 204841
 
7.0%
l 195614
 
6.7%
n 184257
 
6.3%
r 181813
 
6.2%
u 176751
 
6.0%
o 167476
 
5.7%
t 150099
 
5.1%
Other values (38) 724410
24.6%

taxonRank
Text

Missing 

Distinct28
Distinct (%)< 0.1%
Missing4197770
Missing (%)92.9%
Memory size34.5 MiB
2025-03-26T16:25:08.755564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.875477026
Min length2

Characters and Unicode

Total characters2515640
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowvariety
2nd rowVariety
3rd rowvariety
4th rowsubspecies
5th rowVariety
ValueCountFrequency (%)
variety 207439
64.9%
subspecies 101080
31.6%
forma 8243
 
2.6%
var 2270
 
0.7%
form 85
 
< 0.1%
subvariety 81
 
< 0.1%
aff 73
 
< 0.1%
nothosubsp 57
 
< 0.1%
agg 18
 
< 0.1%
fo 18
 
< 0.1%
Other values (15) 63
 
< 0.1%
2025-03-26T16:25:08.834919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 409703
16.3%
i 308608
12.3%
s 303454
12.1%
a 218162
8.7%
r 218158
8.7%
t 207592
8.3%
y 207526
8.2%
v 181991
7.2%
u 101234
 
4.0%
b 101227
 
4.0%
Other values (16) 257985
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2515640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 409703
16.3%
i 308608
12.3%
s 303454
12.1%
a 218162
8.7%
r 218158
8.7%
t 207592
8.3%
y 207526
8.2%
v 181991
7.2%
u 101234
 
4.0%
b 101227
 
4.0%
Other values (16) 257985
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2515640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 409703
16.3%
i 308608
12.3%
s 303454
12.1%
a 218162
8.7%
r 218158
8.7%
t 207592
8.3%
y 207526
8.2%
v 181991
7.2%
u 101234
 
4.0%
b 101227
 
4.0%
Other values (16) 257985
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2515640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 409703
16.3%
i 308608
12.3%
s 303454
12.1%
a 218162
8.7%
r 218158
8.7%
t 207592
8.3%
y 207526
8.2%
v 181991
7.2%
u 101234
 
4.0%
b 101227
 
4.0%
Other values (16) 257985
10.3%
Distinct61214
Distinct (%)1.5%
Missing491413
Missing (%)10.9%
Memory size34.5 MiB
2025-03-26T16:25:08.956923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length63
Mean length11.67446664
Min length2

Characters and Unicode

Total characters46998881
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12828 ?
Unique (%)0.3%

Sample

1st rowKunth
2nd row(Seub.) Rohweder
3rd rowPrance
4th row(Nees) Ekman
5th row(Britton ex Rusby) Wiehler
ValueCountFrequency (%)
l 655773
 
7.4%
528173
 
6.0%
ex 294078
 
3.3%
a 184667
 
2.1%
dc 137986
 
1.6%
kunth 108803
 
1.2%
gray 104795
 
1.2%
benth 100421
 
1.1%
sw 88466
 
1.0%
hook 85217
 
1.0%
Other values (10671) 6517267
74.0%
2025-03-26T16:25:09.164370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 5757767
 
12.3%
4779862
 
10.2%
e 2842027
 
6.0%
r 2155696
 
4.6%
a 1899517
 
4.0%
l 1878374
 
4.0%
n 1788988
 
3.8%
( 1677338
 
3.6%
) 1677338
 
3.6%
o 1598986
 
3.4%
Other values (105) 20942988
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46998881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 5757767
 
12.3%
4779862
 
10.2%
e 2842027
 
6.0%
r 2155696
 
4.6%
a 1899517
 
4.0%
l 1878374
 
4.0%
n 1788988
 
3.8%
( 1677338
 
3.6%
) 1677338
 
3.6%
o 1598986
 
3.4%
Other values (105) 20942988
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46998881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 5757767
 
12.3%
4779862
 
10.2%
e 2842027
 
6.0%
r 2155696
 
4.6%
a 1899517
 
4.0%
l 1878374
 
4.0%
n 1788988
 
3.8%
( 1677338
 
3.6%
) 1677338
 
3.6%
o 1598986
 
3.4%
Other values (105) 20942988
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46998881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 5757767
 
12.3%
4779862
 
10.2%
e 2842027
 
6.0%
r 2155696
 
4.6%
a 1899517
 
4.0%
l 1878374
 
4.0%
n 1788988
 
3.8%
( 1677338
 
3.6%
) 1677338
 
3.6%
o 1598986
 
3.4%
Other values (105) 20942988
44.6%